https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&feed=atom&action=historyPRE2017 4 Groep1 - Revision history2024-03-28T20:06:40ZRevision history for this page on the wikiMediaWiki 1.39.5https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&diff=61955&oldid=prevS150248: /* Uncertainty */2018-06-24T21:04:03Z<p><span dir="auto"><span class="autocomment">Uncertainty</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 22:04, 24 June 2018</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>''Note: We were not able to implement the uncertainty model in the script. This is due to the fact that all of these implementation are done on the raw signal coming in. We have no such data and we are working with software, no hardware attached. As every person has different baseline measurements as well, no concrete calculations can be done from other available data. For these reasons, the further explained model will only be described, with incomplete data, but not implemented in the actual script.''</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>''Note: We were not able to implement the uncertainty model in the script. This is due to the fact that all of these implementation are done on the raw signal coming in. We have no such data and we are working with software, no hardware attached. As every person has different baseline measurements as well, no concrete calculations can be done from other available data. For these reasons, the further explained model will only be described, with incomplete data, but not implemented in the actual script.''</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>As of now, the prediction model is a simple regression of the n-th order (user can decide). This gives a fast real time prediction of values based on prior data. However, a simple regression is not very reliable when it comes to prediction, and even though in the very near future (t + 60s) it might do <del style="font-weight: bold; text-decoration: none;">it’s </del>job very well and extremely fast compared to other methods, the use case for such a near future accuracy is practically non-existent as by that time, it is too late to prevent an injury or further harm to the user. We realized that we needed something better for the algorithm to be actually useful for our users. <del style="font-weight: bold; text-decoration: none;">Alos</del>, as this project is working with different sensors, all having different sensitivities and errors, a predictive filter and probability model will be needed. The suit becomes very cumbersome once the false positives begin to flood in, and false negatives are even worse as this can be a life or death decision factor. These problems hinders the user, but also the rescue post. Furthermore, preventive care is favoured over acute care. Therefore, the algorithm should be able to predict what could happen at least 15 minutes beforehand so the right actions can be taken. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>As of now, the prediction model is a simple regression of the n-th order (user can decide). This gives a fast real time prediction of values based on prior data. However, a simple regression is not very reliable when it comes to prediction, and even though in the very near future (t + 60s) it might do <ins style="font-weight: bold; text-decoration: none;">its </ins>job very well and extremely fast compared to other methods, the use case for such a near future accuracy is practically non-existent as by that time, it is too late to prevent an injury or further harm to the user. We realized that we needed something better for the algorithm to be actually useful for our users. <ins style="font-weight: bold; text-decoration: none;">Also</ins>, as this project is working with different sensors, all having different sensitivities and errors, a predictive filter and probability model will be needed. The suit becomes very cumbersome once the false positives begin to flood in, and false negatives are even worse as this can be a life or death decision factor. These problems hinders the user, but also the rescue post. Furthermore, preventive care is favoured over acute care. Therefore, the algorithm should be able to predict what could happen at least 15 minutes beforehand so the right actions can be taken. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>We have found promising research, with a particular study that set out to predict core body temperature based on earlier data of core body temperature only (Gibrok, Buller, Reed, Hoyt & Reifman, 2010). Their incentive was the fact that military personnel deployed in tropical or desert climates suffer from heat illnesses, and for this reason, the U.S. Army is developing a system to detect such non-combat -and combat- related injuries. It started as a prediction model for core temprature (Gibrok, Mckenna, Reifman, 2006) and then was expanded to incorporate prediction errors to indicate reliability (Gibrok, Buller, Hoyt & Reifman, 2007). Initially, a Butterworth filter is used to predict temperature offline, based on any individuals’ data. It provided accurate predictions because of the ability to incorporate future <del style="font-weight: bold; text-decoration: none;">datapoints </del>as it was implemented offline. It is however crucial, for the suit as well as the U.S. Army prediction system, to be able to predict in real time. Therefore, Gibrok et al. have implemented a Butterworth zero-phase, low-pass filter. As the only difference of using this algorithm for other sensors would be the coefficients and vectors, we can use the same algorithm to predict the other sensor readings.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>We have found promising research, with a particular study that set out to predict core body temperature based on earlier data of core body temperature only (Gibrok, Buller, Reed, Hoyt & Reifman, 2010). Their incentive was the fact that military personnel deployed in tropical or desert climates suffer from heat illnesses, and for this reason, the U.S. Army is developing a system to detect such non-combat -and combat- related injuries. It started as a prediction model for core temprature (Gibrok, Mckenna, Reifman, 2006) and then was expanded to incorporate prediction errors to indicate reliability (Gibrok, Buller, Hoyt & Reifman, 2007). Initially, a Butterworth filter is used to predict temperature offline, based on any individuals’ data. It provided accurate predictions because of the ability to incorporate future <ins style="font-weight: bold; text-decoration: none;">data points </ins>as it was implemented offline. It is however crucial, for the suit as well as the U.S. Army prediction system, to be able to predict in real time. Therefore, Gibrok et al. have implemented a Butterworth zero-phase, low-pass filter. As the only difference of using this algorithm for other sensors would be the coefficients and vectors, we can use the same algorithm to predict the other sensor readings.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>To have a real time core temperature prediction algorithm, data filtering and a predictive model are needed. Filtering can be simply done by putting a low pass filter in the circuitry. Here, a Butterworth zero-phase low pass filter will be used at a cutoff frequency of 42.5 mHz (Gribok, Buller, Hoyt & Reifman, 2010). The cutoff frequency is based on the analysis of the power spectrum of the core temperature, such that approximately 99% of the variance was contained in the range below the cutoff frequency. A Butterworth filter has the best smoothing results in the passband in the offline algorithm (Gribok, Buller, Hoyt & Reifman, 2010). In this article, a Butterworth filter with order 5 has been used. Order five means that the filter contains more circuitry, thus more capacitors and inductors to filter better. The transfer function of this filter (Electronics Tutorials Team, n.d.). is as follows:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>To have a real time core temperature prediction algorithm, data filtering and a predictive model are needed. Filtering can be simply done by putting a low pass filter in the circuitry. Here, a Butterworth zero-phase low pass filter will be used at a cutoff frequency of 42.5 mHz (Gribok, Buller, Hoyt & Reifman, 2010). The cutoff frequency is based on the analysis of the power spectrum of the core temperature, such that approximately 99% of the variance was contained in the range below the cutoff frequency. A Butterworth filter has the best smoothing results in the passband in the offline algorithm (Gribok, Buller, Hoyt & Reifman, 2010). In this article, a Butterworth filter with order 5 has been used. Order five means that the filter contains more circuitry, thus more capacitors and inductors to filter better. The transfer function of this filter (Electronics Tutorials Team, n.d.). is as follows:</div></td></tr>
</table>S150248https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&diff=61954&oldid=prevS150248: /* Problems/Future improvements */2018-06-24T21:01:46Z<p><span dir="auto"><span class="autocomment">Problems/Future improvements</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 22:01, 24 June 2018</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====Problems/Future improvements====</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====Problems/Future improvements====</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>This is a <del style="font-weight: bold; text-decoration: none;">informal </del>list of features that would have been nice but we were not able to implement due to time constraints<del style="font-weight: bold; text-decoration: none;">. Since the proof of concept is only for presentation purposes and none of this will be used on the actual suit, even if this project was continued these points still don’t have priority</del>.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>This is a list of features that would have been nice but we were not able to implement due to time constraints. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Denoising of data, for better derivatives. This could be a simple gaussian denoise.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Denoising of data, for better derivatives. This could be a simple gaussian denoise.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Choosing to load some actual medical datasets to test the algorithm on actual data.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Choosing to load some actual medical datasets to test the algorithm on actual data.</div></td></tr>
</table>S150248https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&diff=61953&oldid=prevS150248: /* The interface (in script.js) */2018-06-24T20:59:25Z<p><span dir="auto"><span class="autocomment">The interface (in script.js)</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:59, 24 June 2018</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1045">Line 1,045:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>For the interface, we decided to use HTML/Javascript since we had experience using it, and since it allows for rapid prototyping. There is also extensive library support. We decided not to use a framework, since there was no framework both Maurits and Jelte knew and the interface was simple enough that a framework was not needed.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>For the interface, we decided to use HTML/Javascript since we had experience using it, and since it allows for rapid prototyping. There is also extensive library support. We decided not to use a framework, since there was no framework both Maurits and Jelte knew and the interface was simple enough that a framework was not needed.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The layout was designed to show everything concisely on one without scrolling. Every graph got a even share of the screen space. The design is not responsive. Graphs are drawn using the canvas API, specifically the 2d context. It provides easy function for drawing lines in various styles, and we did not have to worry about this. It was also easy to draw the legend and other visual effects.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The layout was designed to show everything concisely on one <ins style="font-weight: bold; text-decoration: none;">page </ins>without scrolling. Every graph got a even share of the screen space. The design is not responsive. Graphs are drawn using the canvas API, specifically the 2d context. It provides easy function for drawing lines in various styles, and we did not have to worry about this. It was also easy to draw the legend and other visual effects.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In the real prototype, sensor data will come from actual sensors. However, since we have no sensors, and also because we want to quickly show how our detection works without having to do complex physical tasks, we needed a way to enter historic sensor data. At first, we tried using sliders and a record button that would store data that came from the sliders. However, it proved to be quite difficult to control precisely, plus the difficulty of moving multiple sliders at the same time. We also tried a system where you could enter a “from” and a “to” input for each sensor value and we would automatically generate noisy data. However this did not allow us to input the more complex quadratic curves some sensors typically showed. Thus, we settled for a system where you could “draw” the historic data straight on the graph. The main advantage is that you can easily draw complex curves, and remember what curves with a certain effect look like. The disadvantage is getting exact values is difficult, and that this would need to be implemented from scratch, since most graphing libraries don’t support this.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In the real prototype, sensor data will come from actual sensors. However, since we have no sensors, and also because we want to quickly show how our detection works without having to do complex physical tasks, we needed a way to enter historic sensor data. At first, we tried using sliders and a record button that would store data that came from the sliders. However, it proved to be quite difficult to control precisely, plus the difficulty of moving multiple sliders at the same time. We also tried a system where you could enter a “from” and a “to” input for each sensor value and we would automatically generate noisy data. However this did not allow us to input the more complex quadratic curves some sensors typically showed. Thus, we settled for a system where you could “draw” the historic data straight on the graph. The main advantage is that you can easily draw complex curves, and remember what curves with a certain effect look like. The disadvantage is getting exact values is difficult, and that this would need to be implemented from scratch, since most graphing libraries don’t support this.</div></td></tr>
</table>S150248https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&diff=61952&oldid=prevS150248: /* The interface (in script.js) */2018-06-24T20:58:08Z<p><span dir="auto"><span class="autocomment">The interface (in script.js)</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:58, 24 June 2018</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1043">Line 1,043:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====The interface (in script.js)====</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====The interface (in script.js)====</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>For the interface, we decided to use HTML/Javascript since <del style="font-weight: bold; text-decoration: none;">both Maurits and Jelte </del>had experience using it, and since it allows for rapid prototyping. There is also extensive library support. We decided not to use a framework, since there was no framework both Maurits and Jelte knew and the interface was simple enough that a framework was not needed.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>For the interface, we decided to use HTML/Javascript since <ins style="font-weight: bold; text-decoration: none;">we </ins>had experience using it, and since it allows for rapid prototyping. There is also extensive library support. We decided not to use a framework, since there was no framework both Maurits and Jelte knew and the interface was simple enough that a framework was not needed.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The layout was designed to show everything concisely on one without scrolling. Every graph got a even share of the screen space. The design is not responsive. Graphs are drawn using the canvas API, specifically the 2d context. It provides easy function for drawing lines in various styles, and we did not have to worry about this. It was also easy to draw the legend and other visual effects.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The layout was designed to show everything concisely on one without scrolling. Every graph got a even share of the screen space. The design is not responsive. Graphs are drawn using the canvas API, specifically the 2d context. It provides easy function for drawing lines in various styles, and we did not have to worry about this. It was also easy to draw the legend and other visual effects.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>In the real prototype, sensor data will come from actual sensors. However, since we have no sensors, and also because we want to quickly show how our detection works without having to do complex physical tasks, we needed a way to enter historic sensor data. At first, we tried using sliders and a record button that would store data that came from the sliders. However, it proved to be quite difficult to control precisely, plus the difficulty of moving multiple sliders at the same time. We also tried a system where you could enter a “from” and a “to” input for each sensor value and we would automatically generate noisy data. However this did not allow us to input the more complex quadratic curves some sensors typically showed. Thus, we settled for a system where you could “draw” the historic data straight on the graph. The main advantage is that you can easily draw complex curves, and remember what curves with a certain effect look like. The disadvantage is getting exact values is difficult, and that this would need to be implemented from scratch, since most graphing libraries don’t support this. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>In the real prototype, sensor data will come from actual sensors. However, since we have no sensors, and also because we want to quickly show how our detection works without having to do complex physical tasks, we needed a way to enter historic sensor data. At first, we tried using sliders and a record button that would store data that came from the sliders. However, it proved to be quite difficult to control precisely, plus the difficulty of moving multiple sliders at the same time. We also tried a system where you could enter a “from” and a “to” input for each sensor value and we would automatically generate noisy data. However this did not allow us to input the more complex quadratic curves some sensors typically showed. Thus, we settled for a system where you could “draw” the historic data straight on the graph. The main advantage is that you can easily draw complex curves, and remember what curves with a certain effect look like. The disadvantage is getting exact values is difficult, and that this would need to be implemented from scratch, since most graphing libraries don’t support this.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====The regression (script.js and regression.js)====</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>====The regression (script.js and regression.js)====</div></td></tr>
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</table>S150248https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&diff=61950&oldid=prevS150248: /* Health Warnings */2018-06-24T20:57:26Z<p><span dir="auto"><span class="autocomment">Health Warnings</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:57, 24 June 2018</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>"We found that the magnitude of increase in core temperature and heart rate and the decline in stroke volume were directly related to the body weight loss (and thus dehydration accrued) during exercise. Thus, when subjects exercise at 62% to 67% Vo2max under the present environmental conditions (33°C dry bulb, 50% relative humidity, wind speed 2.5 m/sec), the optimal rate of fluid ingestion to attenuate hyperthermia and cardiovascular drift is the rate that most closely matches fluid loss through sweating, at least until the rate of fluid ingestion replaces 81% of sweat loss."' (Montain, 1992).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>"We found that the magnitude of increase in core temperature and heart rate and the decline in stroke volume were directly related to the body weight loss (and thus dehydration accrued) during exercise. Thus, when subjects exercise at 62% to 67% Vo2max under the present environmental conditions (33°C dry bulb, 50% relative humidity, wind speed 2.5 m/sec), the optimal rate of fluid ingestion to attenuate hyperthermia and cardiovascular drift is the rate that most closely matches fluid loss through sweating, at least until the rate of fluid ingestion replaces 81% of sweat loss."' (Montain, 1992).</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>When the suit detects the onset of hypothermia, the drone can be dispatched to send warmer clothes, heat elements, and perhaps a light-weight tent to serve as shelter and protection. When the situation worsens the person will be warned to not actively heat up <ins style="font-weight: bold; text-decoration: none;">their </ins>body, and a medical team will be sent to offer professional help. When the situation worsens the person will be warned to no longer ingest any food or drink, and try to cool down actively as much as possible and a team of medical <ins style="font-weight: bold; text-decoration: none;">professionnals </ins>will be sent. In conclusion, this means there are several options to detect hypothermia. To detect hypothermia, a galvanic skin response sensor, a heart rate sensor, and a temperature sensor will be <ins style="font-weight: bold; text-decoration: none;">used</ins>. </div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>When the suit detects the onset of hypothermia, the drone can be dispatched to send warmer clothes, heat elements, and perhaps a light-weight tent to serve as shelter and protection. When the situation worsens the person will be warned to not actively heat up <del style="font-weight: bold; text-decoration: none;">theri </del>body, and a medical team will be sent to offer professional help<del style="font-weight: bold; text-decoration: none;">. In the case of hyperthermia the drone can be dispatched to send sports drinks to replenish sodium and bring cooling elements</del>. When the situation worsens the person will be warned to no longer ingest any food or drink, and try to cool down actively as much as possible and a team of medical <del style="font-weight: bold; text-decoration: none;">professionnels </del>will be sent. In conclusion, this means there are several options to detect <del style="font-weight: bold; text-decoration: none;">hyperthermia and </del>hypothermia. To detect hypothermia, a galvanic skin response sensor, a heart rate sensor, and a temperature sensor will be <del style="font-weight: bold; text-decoration: none;">utilised. To detect hyperthermia, the galvanic skin response sensor, heart rate sensor, temperature sensor, and sodium level sensor will be utilised</del>.</div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>To combine these sensor readings into a more personal advice, acceptable, risky, and dangerous ranges for each sensor will be compared and combined. When at least two sensors are indicating a risk, a warning will be issued to the wearer, and the wearer will be given the option to decline the dispatch of a drone to send first aid material. When the wearer declines, another warning and drone request will be sent and offered for declination each time another sensor enters the risky range, or a sensor enters the dangerous range. When two or more sensors reach the dangerous range, a request for professional medical attention will be issued and offered for declination.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>To combine these sensor readings into a more personal advice, acceptable, risky, and dangerous ranges for each sensor will be compared and combined. When at least two sensors are indicating a risk, a warning will be issued to the wearer, and the wearer will be given the option to decline the dispatch of a drone to send first aid material. When the wearer declines, another warning and drone request will be sent and offered for declination each time another sensor enters the risky range, or a sensor enters the dangerous range. When two or more sensors reach the dangerous range, a request for professional medical attention will be issued and offered for declination.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>For the temperature sensor, values in the between baseline - 1 and baseline + 0.5 are ok; values between baseline -1.5 and baseline -1, and baseline +1 and baseline + 1.5 are risky; and values between baseline -2 and baseline -1.5, and baseline +1.5 and +3 are dangerous.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>For the temperature sensor, values in the between baseline - 1 and baseline + 0.5 are ok; values between baseline -1.5 and baseline -1, and baseline +1 and baseline + 1.5 are risky; and values between baseline -2 and baseline -1.5, and baseline +1.5 and +3 are dangerous.</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"></del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">For the sodium level sensor, The ok range is between 140 and 145, the risky range is between 136 and 140, the dangerous range is below 136.</del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>For the galvanic skin response sensor, provided the person is still active which is determined using the heart rate sensor, the ok range is between the midpoint of the baseline measurement and the base of when the person is active and sweating, and the base of when the person is active and sweating. The risky point is between this midpoint and the baseline, and the dangerous range is below the baseline.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>For the galvanic skin response sensor, provided the person is still active which is determined using the heart rate sensor, the ok range is between the midpoint of the baseline measurement and the base of when the person is active and sweating, and the base of when the person is active and sweating. The risky point is between this midpoint and the baseline, and the dangerous range is below the baseline.</div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Nes et al. (2013) have developed an equation to calculate the maximum heart rate of a person. This is: HRmax = 211 − (0.64 × age). This is not the only way to calculate this. There are a lot of other equations which have almost similar results as Nes et al. For this project, we have used this one as it is the most general one. If time would have permitted, more accurate equations would have been used which are, e.g, dependent on the gender and age as well.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Nes et al. (2013) have developed an equation to calculate the maximum heart rate of a person. This is: HRmax = 211 − (0.64 × age). This is not the only way to calculate this. There are a lot of other equations which have almost similar results as Nes et al. For this project, we have used this one as it is the most general one. If time would have permitted, more accurate equations would have been used which are, e.g, dependent on the gender and age as well.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"> </del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Height related issues'''</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Height related issues'''</div></td></tr>
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</table>S150248https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&diff=61946&oldid=prevS150248: /* Health Warnings */2018-06-24T20:52:22Z<p><span dir="auto"><span class="autocomment">Health Warnings</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:52, 24 June 2018</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Health Warnings ===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== Health Warnings ===</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'''Hyper- and Hypothermia''' are some of the most common conditions during mountaineering. For that reason, we have elected to focus our preliminary algorithm on this condition. Specifically, sensor readings for the temperature, heart rate and galvanic skin response are needed to make an appropriate estimation of this condition<del style="font-weight: bold; text-decoration: none;">. Considering the encountered time-constraints, only the before mentioned most important three sensors were implemented in the prototype. Any other sensors below have not been implemented for the purposes of the prototype</del>. When taken separately they can give an approximation of someone’s well being, mostly using averages and estimations. However, as everyone is different, for our application it is important to get a more accurate estimation of someone’s body’s state to base our warnings and actions on.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'''Hyper- and Hypothermia''' are some of the most common conditions during mountaineering. For that reason, we have elected to focus our preliminary algorithm on this condition. Specifically, sensor readings for the temperature, heart rate and galvanic skin response are needed to make an appropriate estimation of this condition. When taken separately they can give an approximation of someone’s well being, mostly using averages and estimations. However, as everyone is different, for our application it is important to get a more accurate estimation of someone’s body’s state to base our warnings and actions on.</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">''Considering the encountered time-constraints, only the before mentioned most important three sensors were implemented in the prototype. Any other sensors researched below have not been implemented for the purposes of the prototype. (yet are still valuable for the final SmartSuit) '' </ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>To get more accurate readings, the sensors will start with baseline measurements of the person in rest. This is to determine their average body temperature and galvanic skin response. For body temperature this is done because the range of possible base temperatures ranges on average from 36.1 to 37.2 degrees centigrade (MedLine Plus team, 2018), and a shift of one degree centigrade in either direction is already enough for a warning; if a person’s core temperature drops by 2 degree centigrade they are already experiencing a mild hypothermia (after a degree drop they experience a severe hypothermia and need medical attention). If a person’s core temperature increases by 1 to 1.5 degrees they either have a fever, or a mild case of hyperthermia. If a person’s body temperature increases by 1.5 to 3 degrees they are experiencing serious hyperthermia, and are in need of medical attention (Calvin, 2018).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>To get more accurate readings, the sensors will start with baseline measurements of the person in rest. This is to determine their average body temperature and galvanic skin response. For body temperature this is done because the range of possible base temperatures ranges on average from 36.1 to 37.2 degrees centigrade (MedLine Plus team, 2018), and a shift of one degree centigrade in either direction is already enough for a warning; if a person’s core temperature drops by 2 degree centigrade they are already experiencing a mild hypothermia (after a degree drop they experience a severe hypothermia and need medical attention). If a person’s core temperature increases by 1 to 1.5 degrees they either have a fever, or a mild case of hyperthermia. If a person’s body temperature increases by 1.5 to 3 degrees they are experiencing serious hyperthermia, and are in need of medical attention (Calvin, 2018).</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l960">Line 960:</td>
<td colspan="2" class="diff-lineno">Line 962:</td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:Hypothermia.PNG]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:Hypothermia.PNG]]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'' '''Figure 12:''' Accidental hypothermia in severe sepsis. ''</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'' '''Figure 12:''' Accidental hypothermia in severe sepsis <ins style="font-weight: bold; text-decoration: none;">(Pathological Society, 2014)</ins>. ''</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The graph suggests the relationship between inflammation and temperature in severe sepsis. As inflammation increases, temperature accordingly increases. After the point of ‘critical inflammation’ core body temperature drops, this may be an adaptive response to prevent further damage mediated through inflammation.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The graph suggests the relationship between inflammation and temperature in severe sepsis. As inflammation increases, temperature accordingly increases. After the point of ‘critical inflammation’ core body temperature drops, this may be an adaptive response to prevent further damage mediated through inflammation.</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l969">Line 969:</td>
<td colspan="2" class="diff-lineno">Line 971:</td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Even these measures are estimates based on averages however, and need to be combined with more sensor data for a more accurate warning system. People experiencing dehydration, hypothermia, or hyperthermia have a reduced galvanic skin response, because they stop sweating and their skin dries out. Galvanic skin response tends to rise when sweating, as sweat increases conductivity, and tends to drop when the skin dries up. Activities and movements such as sports can also influence galvanic skin response readings, but these can be attributed accordingly when other sensor data is taken into account (Shariff, Hingorani, Albadawi, 2015), such as heart rate data, which during exercise should be between 50 to 85 % of 220-age (Gholipour, 2018) (This heart rate data will also be used to alarm professional help in case of cardiovascular events). Under hypothermia conditions a person’s heart rate will drop, under hyperthermia conditions a person’s heart rate will initially rise, and eventually drop as the person goes into shock.When most or all spikes in galvanic skin response have been attributed to other causes such as physical activity, what is left is the base galvanic skin response. When this is base higher on average than the baseline measurement, this is indicative of a sweaty skin, when this base is lower on average than the baseline measurement, this means the skin is drying out. There is no such data found on the exact values of when things get dangerous, but what could be done is looking at the derivatives of the increase/decrease of the heart hate associated with hyper/hypothermia.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Even these measures are estimates based on averages however, and need to be combined with more sensor data for a more accurate warning system. People experiencing dehydration, hypothermia, or hyperthermia have a reduced galvanic skin response, because they stop sweating and their skin dries out. Galvanic skin response tends to rise when sweating, as sweat increases conductivity, and tends to drop when the skin dries up. Activities and movements such as sports can also influence galvanic skin response readings, but these can be attributed accordingly when other sensor data is taken into account (Shariff, Hingorani, Albadawi, 2015), such as heart rate data, which during exercise should be between 50 to 85 % of 220-age (Gholipour, 2018) (This heart rate data will also be used to alarm professional help in case of cardiovascular events). Under hypothermia conditions a person’s heart rate will drop, under hyperthermia conditions a person’s heart rate will initially rise, and eventually drop as the person goes into shock.When most or all spikes in galvanic skin response have been attributed to other causes such as physical activity, what is left is the base galvanic skin response. When this is base higher on average than the baseline measurement, this is indicative of a sweaty skin, when this base is lower on average than the baseline measurement, this means the skin is drying out. There is no such data found on the exact values of when things get dangerous, but what could be done is looking at the derivatives of the increase/decrease of the heart hate associated with hyper/hypothermia.</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"></del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">In the specific conditions our device will be used in hyperthermia seems like a less likely condition, but it can actually still happen through other causes than a high environmental temperature. Another cause for hyperthermia is overexertion. In overexertion the body has produced more heat than it can handle (rather than receiving it from the environment), and will start to sweat until it can no longer do so, making it similar as the other situation yet still relevant to our use case. Another way to therefore detect this situation is to signal a dehydration or hyponatremia in time. A dehydration is a lack of fluids in the body, and hyponatremia is a lack of salts in the body. In the case of hyperthermia the dehydration is caused by excessive sweating, which goes paired with a large loss of salts in the body, meaning in the specific case of hyperthermia, dehydration and hyponatremia go together, and can be predicted by monitoring the amount of salts in a person’s blood. This can be measured using blood sensors mentioned in previous weeks. Blood sodium levels ought to stay between 135 and 145 milliequivalents per liter (Mayo Clinic Staff, 2018); when they fall below this, a person has hyponatremia.</del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:biketemp.jpg]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:biketemp.jpg]]</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l982">Line 982:</td>
<td colspan="2" class="diff-lineno">Line 982:</td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:BodyTempMin.jpg]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:BodyTempMin.jpg]]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'' '''Figure 14:''' Heart rate and body temperature measurements for walking into running into sitting ''</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'' '''Figure 14: '''Heart rate and body temperature measurements for walking into running into sitting <ins style="font-weight: bold; text-decoration: none;">(Iwasaki, 2015). </ins>''</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In this figure, the heart rate and body temperature have been measured for a person who went from walking (at time 0), to running (at time 5 min), to sitting (at time 35). The different curves show different speeds. The blue curve corresponds to running at 6km/h, red to 8km/h and green to 10km/h. Error bars have been included in this figure meaning that several people participated in this research. So note: Two of the seven subjects gave up 15 and 20 min after running started at 10 km/h because of difficulty maintaining the pace. Therefore, seven datasets collected at each time point, except for 20 min at 10 km/h (n= 6) and from 25 min to 34 min at 10 km/h (n=5) (Iwasaki, 2015).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In this figure, the heart rate and body temperature have been measured for a person who went from walking (at time 0), to running (at time 5 min), to sitting (at time 35). The different curves show different speeds. The blue curve corresponds to running at 6km/h, red to 8km/h and green to 10km/h. Error bars have been included in this figure meaning that several people participated in this research. So note: Two of the seven subjects gave up 15 and 20 min after running started at 10 km/h because of difficulty maintaining the pace. Therefore, seven datasets collected at each time point, except for 20 min at 10 km/h (n= 6) and from 25 min to 34 min at 10 km/h (n=5) (Iwasaki, 2015).</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l999">Line 999:</td>
<td colspan="2" class="diff-lineno">Line 999:</td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'' '''Figure 16:''' Relative dehydration risks (Tuned into Cycling Team, 2012) ''</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'' '''Figure 16:''' Relative dehydration risks (Tuned into Cycling Team, 2012) ''</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>A galvanic skin response detector is able to measure a person’s hydration level. It is expressed in gain (dB). Figure 17 shows simulated sensitivity. One can see that after drinking, the gain will increase accordingly. This is just a proof <del style="font-weight: bold; text-decoration: none;">that </del>galvanic skin responses work. No further useful conclusions with respect to this project can be made with this information.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>A galvanic skin response detector is able to measure a person’s hydration level. It is expressed in gain (dB). Figure 17 shows simulated sensitivity. One can see that after drinking, the gain will increase accordingly. This is just a proof <ins style="font-weight: bold; text-decoration: none;">how </ins>galvanic skin responses work. No further useful conclusions with respect to this project can be made with this information.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:Galvanic2.JPG]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:Galvanic2.JPG]]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'' '''Figure 17:''' Simulated galvanic skin response sensor sensitivity ''</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'' '''Figure 17: '''Simulated galvanic skin response sensor sensitivity <ins style="font-weight: bold; text-decoration: none;">(Asogwa, 2016).</ins>''</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'' '''Table 6:''' Concequences of dehydration (Montain, 1992) ''</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'' '''Table 6:''' Concequences of dehydration (Montain, 1992) ''</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:Physiological.PNG]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:Physiological.PNG]]</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"></del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"></del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">“Although the symptoms of heat illness can vary widely among individuals, heat exhaustion due to dehydration is often evidenced by irritability, sudden fatigue, and lightheadedness, with nausea and headache also possible. Skin color is often pale with normal-to-profuse sweating. Heat stroke is characterized by high core temperature, reddened skin, and normal-to-profuse sweating. Severe heat stroke is characterized by central nervous system dysfunction (eg, loss of motor coordination, delirium), and, in the most serious cases, loss of consciousness leading to coma.“ (Murray, 1996)</del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>"We found that the magnitude of increase in core temperature and heart rate and the decline in stroke volume were directly related to the body weight loss (and thus dehydration accrued) during exercise. Thus, when subjects exercise at 62% to 67% Vo2max under the present environmental conditions (33°C dry bulb, 50% relative humidity, wind speed 2.5 m/sec), the optimal rate of fluid ingestion to attenuate hyperthermia and cardiovascular drift is the rate that most closely matches fluid loss through sweating, at least until the rate of fluid ingestion replaces 81% of sweat loss."' (Montain, 1992).</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>"We found that the magnitude of increase in core temperature and heart rate and the decline in stroke volume were directly related to the body weight loss (and thus dehydration accrued) during exercise. Thus, when subjects exercise at 62% to 67% Vo2max under the present environmental conditions (33°C dry bulb, 50% relative humidity, wind speed 2.5 m/sec), the optimal rate of fluid ingestion to attenuate hyperthermia and cardiovascular drift is the rate that most closely matches fluid loss through sweating, at least until the rate of fluid ingestion replaces 81% of sweat loss."' (Montain, 1992).</div></td></tr>
</table>S150248https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&diff=61931&oldid=prevS150248: /* The SmartSuit */2018-06-24T20:37:38Z<p><span dir="auto"><span class="autocomment">The SmartSuit</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:37, 24 June 2018</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== The SmartSuit ===</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== The SmartSuit ===</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">The </del>final visual design for our SmartSuit <del style="font-weight: bold; text-decoration: none;">is given below</del>. For the sake of branding, we have named our design the 'Hiketech SmartSuit'.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Based on all research, all processors, wires and sensors selected, the </ins>final visual design for our SmartSuit <ins style="font-weight: bold; text-decoration: none;">has been created</ins>. For the sake of branding, we have named our design the 'Hiketech SmartSuit'.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:Hiketech_wired2.0.jpg|600px|SmartSuit Design (With wiring)]] </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:Hiketech_wired2.0.jpg|600px|SmartSuit Design (With wiring)]] </div></td></tr>
</table>S150248https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&diff=61928&oldid=prevS150248: /* Wiring */2018-06-24T20:35:08Z<p><span dir="auto"><span class="autocomment">Wiring</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:35, 24 June 2018</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==== Wiring ====</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==== Wiring ====</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Another important part of the SmartSuit is the wiring. <del style="font-weight: bold; text-decoration: none;">As already explained in the Base Architecture, we </del>have opted to use wires (with bluetooth as backup). However, it is imperative that the wires are sturdy and cannot easily get damaged. We have therefore researched several options.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Another important part of the SmartSuit is the wiring. <ins style="font-weight: bold; text-decoration: none;">We </ins>have opted to use wires (with bluetooth as backup)<ins style="font-weight: bold; text-decoration: none;">, as has been shown from the survey as well</ins>. However, it is imperative that the wires are sturdy and cannot easily get damaged. We have therefore researched several options.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Currently Available'''</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''Currently Available'''</div></td></tr>
</table>S150248https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&diff=61927&oldid=prevS150248: /* Sensor details overview */2018-06-24T20:34:08Z<p><span dir="auto"><span class="autocomment">Sensor details overview</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:34, 24 June 2018</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'' '''Table 2:''' Sensor Options''</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'' '''Table 2:''' Sensor Options''</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"></del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{| class="wikitable"</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{| class="wikitable"</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>! Name/Model: </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>! Name/Model: </div></td></tr>
</table>S150248https://cstwiki.wtb.tue.nl/index.php?title=PRE2017_4_Groep1&diff=61926&oldid=prevS150248: /* Processors */2018-06-24T20:33:28Z<p><span dir="auto"><span class="autocomment">Processors</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:33, 24 June 2018</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">As already explained earlier</del>, the <del style="font-weight: bold; text-decoration: none;">SimpleLink </del>and ESP32 have been chosen <del style="font-weight: bold; text-decoration: none;">for our design</del>. <del style="font-weight: bold; text-decoration: none;">This was based on weighing the different advantages named </del>in the <del style="font-weight: bold; text-decoration: none;">table above</del>.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Based on this table</ins>, the <ins style="font-weight: bold; text-decoration: none;">Simplelink </ins>and ESP32 <ins style="font-weight: bold; text-decoration: none;">processors </ins>have been chosen. <ins style="font-weight: bold; text-decoration: none;">The exact argumentation will follow </ins>in the <ins style="font-weight: bold; text-decoration: none;">final SmartSuit architecture section</ins>.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==== Sensors ====</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==== Sensors ====</div></td></tr>
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