PRE2020 3 Group8: Difference between revisions

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The tendency to attribute human features or behaviour to non-human agents is called Anthropomorphism <ref> Guthrie, S. E. (1997). Anthropomorphism: A definition and a theory. </ref>. With human features, emotions are also included. Humans are fundamentally emotional beings, which causes that human communication and social interaction is not purely based on rationalities, but also include emotive or affective variables. Therefore, to truly motivate the end-user to long-term behaviour change, emotional responses must be evoked by the user.  
The tendency to attribute human features or behaviour to non-human agents is called Anthropomorphism <ref> Guthrie, S. E. (1997). Anthropomorphism: A definition and a theory. </ref>. With human features, emotions are also included. Humans are fundamentally emotional beings, which causes that human communication and social interaction is not purely based on rationalities, but also include emotive or affective variables. Therefore, to truly motivate the end-user to long-term behaviour change, emotional responses must be evoked by the user.  


Anthropomorphic design principles are often employed to facilitate interaction and acceptance. In social robotics, this is often done by designing a physical embodiment with human-like features <ref> Breazeal, C., Dautenhahn, K., Kanda, T.: Social robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 1935–1972. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-32552-1_72 </ref>. To make the system easily accessible and feasible for most people of the target group, it has been chosen to design a system for % !!!!! (website/app/???) devices.
Anthropomorphic design principles are often employed to facilitate interaction and acceptance. In social robotics, this is often done by designing a physical embodiment with human-like features <ref> Breazeal, C., Dautenhahn, K., Kanda, T.: Social robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 1935–1972. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-32552-1_72 </ref>. To make the system easily accessible and feasible for most people of the target group, it has been chosen to design a system for existing devices (phone, tablet, computer).




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''' Conclusion '''
''' Conclusion '''


== Conversational Embodied Agents and Chatbots ==
== Conversational Embodied Agents and Chatbots ==

Revision as of 18:50, 11 March 2021


Group description

Abstract

A pure software end-user application that supports people in their need to socialize while motivating self-improvement. Anthropomorphism is intentionally used to increase user commitment and experience. Machine learning techniques are used to process user's data and provide feedback, and to facilitate the anthropomorphized interface.


Conceptualized Idea

Hypothetical idea: A webpage/application, that will motivate its users into a more (mentally) healthy way of living life. The user can put in any preferences to be motivated on and set its own goals. Numeric data of the user will be tracked in a communicative way rather than filling in forms, is stored and compared to older data, and reflection of this comparison will be given in a personalized way back to the user.

On this page, the concept of this motivating coaching system will be presented. This system combines specialized monitored and self-measured data from its user with individual and reference knowledge to give its users an overview (and recommendations). The system will keep track of one’s daily life structure, translate and summarize the numerical input into recommendations in a continuous personalized motivating coaching dialogue taking into account the needs and preferences of each user individually.


Members

Name Student number Department
Edwin Steenkamer 1006712 Computer Science
Emi Kuijpers 1227154 Psychology & Technology
Fanni Egresits 1316400 Psychology & Technology
Morris Boers 1253107 Computer Science
Lulof Pirée 1363638 Computer Science


Main Task division

Task Members
Backend software (database, question scheduling, output) Lulof & Edwin
Graphical User Interface Morris
USE-part Fanni & Emi


GitHub Page:

GitHub


Logbook

See the page logbook_group_8

Problem statement and objectives

Problem Statement

Often loneliness is associated with elderly people living unintentionally in social isolation due to unfortunate circumstances. However, the reality is that loneliness is experienced by all ages and almost all humans. [1] Humans are social animals [2], and we humans influence each other by merely existing together. Loneliness is seen as a severe public health issue due to its association to increased risk of morbidity and mortality [3]. A study by Luhmann & Hawkley [4] suggests that the prevalence rates of loneliness are highest for young adults (<30 years).

Loneliness can best be stated as the perceived discrepancy between the desired amount and the actual perceived amount and quality of social communication and relationships. [5] Both physical, as well as mental health and overall well-being, can suffer from this. With less social relationships, less comparison and facilitation from others will influence a person's behaviour and motivation, leading to a decrease in intrinsic motivation.

Due to the COVID-19 pandemic restrictions, many people are forced to stay at home [6]. These restrictions also cause personal trainers, dieticians, and other employees that help people engage in a more healthy lifestyle to be unable to work with the normal citizen. However, at this very moment, people must maintain a healthy lifestyle. Exercising and healthy lifestyles increase at the beginning of covid. After the restrictions aimed at minimizing the risk of local transmission of SARS-CoV-2 got stricter, it is more likely that this leads to reductions in physical activity. [7] Therefore, it is necessary to find other ways to externally motivate people to maintain a healthy lifestyle independent of other people's motivation.

Besides the external motivation that humans get from other people, we also can motivate ourselves. People have a very complex decision-making process, and not always the rationally right decision is chosen for a particular event or action. Heuristics are used as a mental shortcut to make decisions and are simplifications. These can occasionally lead to systematic flaws and errors, which are deviations from the normative decision-making model, known as biases.

One of these biases is overconfidence, a bias during interpreting and assessing information, the second step in the decision-making process. People are often biased in their confidence concerning the hypothesis they have brought in their working memory, believing that they are more correct more often than they actually are. For example, if people would have to report how much they ran during a jog, they will most likely unconsciously overestimate themselves. [8] There is also the psychological phenomenon called planning fallacy, where people underestimate the time it will take to complete a future task. [9] Combining this will optimism bias, which explains our tendency to overestimate the likelihood of experiencing positive events and underestimate the occurrence of negative events. The human mind can sometimes be a dangerous decision-making toolbox to rely on. Using this system, the data gathered from the user will serve as a toolbox. They can be seen as an extended memory that provides the user with statistically true information so decisions will be made taking into account actual statistics, rather than only the estimations and thoughts humans have left of past events. In this way, users will be calibrated towards the truth and rely less on decisions made while possibly be biased.


Needs

Two needs arise from the problem statement above. Firstly, to reduce biases in the decision-making process due to falsely remembered memories, people need to be provided with an objective summary of their past events. Secondly, the net amount (both intensity and frequency) of negative emotions and lack of motivation experienced due to a lack of social interaction must be decreased to fill the lack of external motivation. A system is needed that enhances intrinsic motivation and also provides personalized social interaction to the user to enhance engagement while using the system.

Goals

To reach the needs stated above, the software application has two main requirements:

  • Firstly, the system's main aim is to provide users with insight in parts of their behavioural patterns that are unknown to them. Humans tend to rely heavily on their intuitions, which can often strongly deviate from its true value. The system will serve as a non-biased evaluation tool, which will result in the user discovering behavioural patterns after a fixed period based on their objective data, rather than intuitions and memory.
  • Secondly, to increase the motivation of the user to put in data, and to enhance positive changes in behavioural patterns, anthropomorphic features will be used while designing the system. Using a human-like question generator to let the user put in data rather than a non-interactive (for example, a questionnaire), the user will perceive a need to answer the system rather than merely filling in the data. The aim here is to increase motivation and overall well-being by presenting the data's reflection in a personalized way.


Beyond the scope

The following features are probably valuable additions to the product, but they are beyond the scope of what can be achieved in one quartile:

  • Voice recognition, natural language text inputs (natural language processing is too much to add within the given timespan)
  • Animated anthropomorphized interface (e.g. simulated face)


Current State-of-the-art

Social Agent Coaches

Exercise

Bickmore and Picard developed a computer-based virtual relational agent that served as a daily exercise advisor by engaging the user in conversation and providing educational information about walking for exercise, asking about the user's daily activity levels, tracking user progress over time while giving feedback, and engaging the user in relational dialog. [10] These kind of social agents are also used often as a weight-loss advisor coach. [11]


Food

TODO


Mental Health

TODO


Sleep

TODO


In the system, the agent is both an administrator and active participant in the health-related activity, resulting in a unique characteristic for the system: The social interaction between the robot and the user is not only useful for maintaining user engagement and influencing intrinsic motivation but is also an instrumental necessity in achieving the healthy behaviour.


Competitor Analysis

  • Still in progress

In conclusion, most AI assistants are used for marketing strategies or for analytical tasks. The complexity of the health and well-being related software is very low. The AI Assistants are mainly used for asking repetitive questions in the form of daily reports and to do generic measurements with Smart bracelets or watches. Compared to these devices, we concluded that our AI would bring new functionalities to the market. In reflection to other AI assistants, _NAME_ uses its personalized questionnaire during the set-up of the software and based on that analysis the performance of the user, gives feedback and guidance with subjective questions during the day. Our team targeted four perspectives and decided to focus on well-being, healthy eating, sports activity and sleeping.

The main functionality that made our product outstanding is the use of the self-reflection technique on the users. After doing market research and conducting questionnaires, we realized the main goal is the increase of motivation. In order to accomplish this, the users reflect on their own performance and answer their own questions such as why they make specific decisions, how their decisions influence them and how they can use their knowledge for their own benefit. During the market research, one of the obvious realizations was that people lose interest just by seeing notifications or comparisons to other users.

Therefore, our product would rely on incorporating strategies from cognitive-behavioural therapy, accepting and committing therapy, mindfulness, and other science-based approaches. This could be the key element to increase intrinsic motivation. The Youper (https://play.google.com/store/apps/details?id=br.com.youper&hl=en_IN) named software stands the closest to our idea, but it focuses on the stress and mindfulness perspectives.

Compared to Youper we would add sport performance measurements and diet helps. As seen in the stakeholder analysis, we would involve additional specialists in the project which would give more opportunities and better reliability. Additionally, Wysa, the interactive penguin assistant provides similar services [12]. The penguin assistant has a highly developed AI system for any kind of mental therapy, although the software deals only with mental well-being. With _NAME_ we tried to combine all the options together and create something that deals with not only mental well-being but also with the physical state.

Design and Approach

While designing the system that will enhance healthy behaviour in a pro-active way, we followed the design methodology, which asserts that the SAR agent must possess [13]:

(1) The system must have the ability to influence the user's intrinsic motivation to perform the task.

(2) The system must have the ability to personalize social interaction to maintain user engagement in the task and build trust in the task-based human-robot relationship.


Only with these characteristics can the system be implemented successfully and be effective in one's life. The two characteristics will be explained below.


Influence the user's intrinsic motivation

First, the system must have the ability to influence the user's intrinsic motivation. Intrinsic motivation comes from a person itself, and it has been shown that intrinsic motivation is more effective for long-term task compliance and actual behaviour change. [14]. Intrinsic motivation does not have to be triggered by the person itself, but can also be affected by external factors, for instance, by a (digital) instructor or coach. Through positive and negative (verbal) feedback, the intrinsic motivation of the user can be affected.

Below, three characteristics of a system that influence intrinsic motivation according to previous studies have been explained briefly, which will later be implemented in our system.


Competition

Humans like to compete against each other and improve themselves, and be challenged to compete against an ideal outcome. Studies show that humans perform better in a competitive environment compared to a non-competitive environment. [15] [16]

Therefore, it is important to consider designing and providing weekly summaries, high scores and comparisons to the user. These concepts tend to increase the intrinsic motivation for the task [17]. The user must be exposed continuously to their performances for motivational purposes. % our system does this by ………..


Reward and Criticism

Both reward and criticism are two forms of feedback a digital coach can give. According to a study conducted by Vallerand and Reid (1984), forms of negative feedback – criticism and punishment – is considered to have a negative impact on the intrinsic motivation of the user [18]. On the other hand, Vallerand also stated in another study (1983) that positive feedback – praise and reward – tend to positively impact the user's intrinsic motivation [19].

However, it is essential to note that the effect of positive feedback is closely tied to the user's own perceived competence at the task. Once a user believes he is doing an excellent job while performing a task, praise and reward are no longer affecting intrinsic motivation.

It is decided that the system will not provide any negative feedback and only praise and reward the user upon correct completion of tasks and when following up the right advice. With this, the user can be helped with getting a healthier lifestyle without a decrease in intrinsic motivation.


Autonomy and Self-determination

To achieve long-term behaviour change, the system should support user autonomy and self-determination [20] Users do not want unnecessary information and do not want to put in data that they think is unnecessary.

Therefore, the user will have the freedom to choose the preferences he/she wants to be advised for. The users can also set the amount and timing of notifications and can also put in questions themselves. User choice and the level of autonomy a user wants differs per system and innovation. This makes self-determination a fascinating concept that must be studied specifically for this system with a user-study.


Personalized Social Interaction to maintain User Engagement

TODO '

Anthropormorphism The tendency to attribute human features or behaviour to non-human agents is called Anthropomorphism [21]. With human features, emotions are also included. Humans are fundamentally emotional beings, which causes that human communication and social interaction is not purely based on rationalities, but also include emotive or affective variables. Therefore, to truly motivate the end-user to long-term behaviour change, emotional responses must be evoked by the user.

Anthropomorphic design principles are often employed to facilitate interaction and acceptance. In social robotics, this is often done by designing a physical embodiment with human-like features [22]. To make the system easily accessible and feasible for most people of the target group, it has been chosen to design a system for existing devices (phone, tablet, computer).



Conclusion

Conversational Embodied Agents and Chatbots

Chatbots can be defined as machine agents that serve as natural language user interfaces for data and service providers [23]. These chatbots can serve a large variety of different roles, but they share common goals: Engaging a conversation to track, educate, encourage or prevent some behavior on the patient [24]. These systems especially influence the behaviour of the younger generation, due to their technology-oriented view of life. Chatbots provide the perfect blend between immediacy and asynchronicity, because of its combination of prompt answers/feedback and notifications/reminders [25]. The user will receive the summarized data in a way that will be perceived as more personal.

An experiment [26] has been conducted (2018) in order to determine the percentage of people that seek professional help when they have troubles with their mental health, exercising, diet, and sleep. Studies show that this percentage is extremely low, which can be caused by many different reasons. People might not feel like their problem is worth seeking help, or think they can fix it on their own, they cannot financially afford it, do not have enough time for it or simply do not think about it. Especially the younger generation first tries using the internet and mobile phone applications before reaching out for professional help. Therefore, the applications offered need to be professional and serve as a digital coach for people who do not want to seek professional help for any reason they provide.

Chatbots are also the perfect system to be perceived as non-judgmental and objective.


Chatbot versus Social Robot

A paper by Andrea Deublein and Birgit Lugrin shows the result of an experiment where social robots are compared to tablets. The results of the study indicate that none of the experimental conditions (social robot // tablet // emotional social robot) was generally perceived superior to the others. Each interface has its own advantages. The tablet performed best for usability and workload, but social perception and overall evaluation were higher rated when there was a more social interface used.

Therefore, combining both positive aspects, Motis is an application that can be used on all devices that are already known by the end-user (phones, tablets, desktops), which will increase the overall usability. This will also decrease the amount of workload for the user.

However, by giving the answers in a way as if the user is communicating with Motis, a social aspect will be implemented in the system. By doing this, Motis will be perceived as more socially engaging and therefore work better as a digital coach to pro-actively change one's behaviour into a more healthy one.

Software Design

The software is divided over four modules:

  1. The Graphical User Interface (GUI). This is the module only the user will directly interact with. It provides a modern interface reminiscent of a modern chat services, together with an abstract anthropomorphized face. The GUI runs in a separate process.
  2. A database. A local SQL database used to store the gathered user information. The SQLite[27] implementation is used, as this provides a lightweight, server-less SQL server.
  3. The data gathering process. In order to provide useful feedback to the user, information must be gathered regularly. This module manages a calendar of reoccurring sets of questions. As soon as their scheduled moment is reached, the question sends the questions to the GUI process, and stores answers in the database. The data gathering also runs as a separate process.
  4. The output generation process. This third process monitors the database and triggers feedback production as soon as criteria are met. Furthermore, it formats the output and attaches an evaluation.

It is of conceptual importance that the data gathered about the user belongs to a certain Variable. Variables can for example be hours of sleep, a mood evaluation, an energy evaluation, number of friends spoken, minutes of exercise, etc. The system is designed such that the set of tracked Variables can easily be changed, even at runtime.

Graphical User Interface (GUI)

Database

Data Gathering Process

Output Generation Process

Rules

The output generation works on the basis of a set of so-called rules. A rule is a mathematical function that compares the values of one or more Variables, and possibly their history, and maps this to a Boolean output (True / False) plus presentable information. The Boolean part of the output indicates whether the observation is worth notifying the user about. The other output is the actual output given to the user, which can be a summary statistic of their data, a notification about a positive or negative trend, a major change in pattern, etc... This output will not be presented directly to a user, but will first be formatted by the Formatter.

The third output of some rules is an evaluation. In its simplest form, this is a decimal number in [-1, 1], where -1 indicates 'bad', 0 'neutral' and 1 'good'. This evaluation is used to tune the formatting of the output, and also the expression of the anthropomorphized face of the GUI will adapt to the 'mood' of this evaluation.

Similarly to Variables, also rules are not hard-coded and can be added or removed at runtime. Note that the set of employed rules must be designed carefully, in consideration of psychological/behavioural research, for a successful application of the software.

The use of rules is a tradeoff between feasibility and performance. Ideally, one would not have to create rules manually (which introduces risks of errors, forgetting to add important rules, etc.) but use an automatic system to decide when and what feedback to provide. Such a system would be able to maximize the impact on the user, and hence the utility of the software. Furthermore, it could add a reliable and detailed evaluation. However, this approach would require an advanced model of human health and cognition, which is far beyond the current state of the art.

Another solution would be to use large amounts of prior data. Either through statistical means or through machine learning, it is possible to accurately map data to an evaluation, and possibly also to appropriate feedback. This would require less domain knowledge of the engineer than designing the rules, but the set of chosen Variables must be fixed and reflect the datasets well. This approach is not used, as it hinders the flexibility of the software, and the required data is not easily gathered within the scope of the project.

Intermediate interface

Individual rules can relatively easily be implemented in the source code, and in a similar way, it is simple to add variables. However, the stakeholders who need to specify the rules are not the programmers. Instead, this will be the domain specialists, such as dietists, personal trainers, and psychologists (see #Stakeholder analysis). It cannot be expected that these people have the time, motivation or experience to read the source code. Hence it was decided to provide a simplified interface for defining rules. This interface is still hidden from the end-users, but simpler for the domain specialists to use without prior programming knowledge.

Access

The domain specialist can specify variables and rules via a text file. For this purpose, a simplified syntax is used. In particular, to reduce ambiguities the following properties have been added:

  • The syntax is case insensitive
  • No manual indexing is needed. This avoids the ambiguity of the first index: this is often 0 in programming languages, whereas the value 1 is often used in mathematics and everyday language.

An even simpler interface would be a GUI with a form or formula-editor, that automatically checks every value entered and allows the user to construct rules by means of buttons for connectives, and drop-down-menus for variables. This remains a possible extension point of the software. Note that it is not in conflict with the syntax specified below: this GUI interface could produce a text file with the simplified syntax.

Defining Variables

Specifying variables is simple, as only a name and a type are needed. Take, for example:


var hours_sleep decimal

This line specifies that a variable 'hours_sleep' needs to be created and that it will have decimal numbers as values.

In general, the variable-defining syntax is:

var [name] [type]

where:

  1. var specifies that the rest of the line describes a Variable.
  2. [name] specifies the name of the variable. The name may not contain whitespaces.
  3. [type] specifies the type of the variable. The following types are recognized:
    1. Decimal numbers: any of decimal, float, num, number
    2. Integer numbers: any of int, integer, wholeNumber
    3. Strings: any of str, string, text

Defining Rules

A rule is defined on a single line of the file. For example:

rule mean(hours_sleep(last 3))>=mean(hours_sleep(allButLast 3)) | mean(hours_sleep(last 3))

In this example, hours_sleep is a Variable. Here the mean of the last 3 entries of 'hours_sleep' is compared to the mean of all entries before. The part after the | indicates the value that will be communicated to the user. In this case, this is the mean value of the last three entries of 'hours_sleep'.


In general, the rule-defining syntax is:

rule [expression 1] [comparison] [expression_2] | [expression_3]

Where:

  1. [expression 1], [expression 2] and [expression 3] are any valid expression, described further below.
  2. [comparison] is one of ==, >=, <=, > or <, and indicates whether [expression 1] must be equal, equal or greater, equal or smaller, greater or smaller than [expression 2], respectively, for the rule to be satisfied.

Expressions contain either Variables, constant numbers or both, and evaluate to a single number. Variables must always be indexed, as they generally speaking have multiple entries. Given a Variable named x, it can be indexed as x([specifier] [num]), where

  1. [specifier] is one of any, allButLast, last, first, allButFirst
  2. [num] is an integer.

Supported binary operations are +, -, /, * and % (which correspond to the usual arithmetic operations, and % to modulus). The unary operation mean(x(...)) is also supported, with returns the sample mean of a subsequence x(...).

User description

Primary Target Audience The application will be designed to be used by technology-oriented adults (mainly focusing on younger than 30 years), who interact with computers and smartphones on a daily basis. The main target for this system are students who spend most of their time alone in their student accommodation. This can both be due to contemporary COVID-19 pandemic stay-at-home regulations, but also for users who live abroad for a short time, for example.

The focus will also be on people who want to improve their daily structure and overall well-being in any way but have no idea what would be best for them. Therefore, the system will provide its user with objective data to discover clear behavioural patterns that might have lead to certain events. Users are more extensively described at the user analysis-section.

As stated before, the focus will be put on the younger generation. Because of their affiliation with technology, it is most certainly easier for them to adapt to systems and be nudged into a healthy behaviour compared to people who first have to accept the device before being able to use the application. Research also states [28] that mental illnesses cause a huge burden on the younger generation's illnesses.

The application will likely not be suitable for children due to its design constraints, or for elderly people who are not technology-oriented and adapted to new technologies.


User requirements

To engage positive behaviour in people using an AI application, an accessible and practical user-interface is critical. An irresponsive, unintuitive or unfinished interface may discourage users from using the application, let alone be positively nudged by the application. Users need to perceive social engagement and be positively motivated by the system to change its behaviour.

User Analysis

Persona Storyboards

Persona 1.jpg

Persona 2.jpg

Persona 3.jpg

Persona 4.jpg

Persona 5.jpg

File:Persona 6.jpg

Approach, milestones and deliverables

Milestones

The project milestones are divided into three main parts: Implementing a human-centered design approach, the system's economical value and the software milestones.

User-centered Design

Target User-perspective: Human-Centered design

  • Perform user analysis
  • Describe the potential main users and their needs
  • Provide several persona scenario's.
  • Define boundaries for user requirements
  • Research needs to be conducted on how different system features will motivate its users and trigger them to change their behaviour in a non-intrusive, engaging way.
  • Secondary end-users and stakeholders (developers, the scientific community) need to be defined and described.


Potential Ethical Threats

  • Negative anthropomorphism: By adding human-like features to non-human agents, it is essential to consider possible threats of cognitive deception. The user must be (and stay) aware that the responses are computationally generated.
  • Paternalism: The system is designed to provide its user's insight into their behaviour and motivate them objectively to make positive changes. Hence, the system is deliberately nudging the user. A sufficient amount of care must be taken not to exploit this capability to act against the user's interest.
  • Intruding one's privacy: Research has to be investigated to find an optimum amount of tradeoff between the user trusting the system enough to give a sufficient amount of data without negatively intruding the user.


Comparative advantage, product innovation and improving Quality of life of its users

  • Essence and uniqueness of the project need to be defined.
  • Describe current state-of-the-art, combining it with essence and uniqueness to state comparative advantages.
  • Define the actual perceived gain of the end-user.

Economical perspective

Economical value

In this section, the preferences of the given users are described that determine the economical value of our product. This can be defined by a survey/questionnaire or by market-research.The economical value of the product is the benefit that the costumers receive from the usage of the AI software. In the case of the specific device we develop, this could be the motivation, joy, health,fun..etc. This value is not an objective characteristic, but rather subjective, since it differs by its user's needs and expectations. Because of this diversity, our proposal is a survey conduction before the start of the program to collect enough information for a fully personalized service. On this way, the economical value to the customers (EVC) can be determined and the market price of the software can be quantified. \\ After the market-research, we plan to create a competitor-analysis which helps us to see which additional tools we need to implement and what the essence and uniqueness our product could be compare to our competitors. This step will also help us to narrow or extend the list of stakeholders we want to approach. This information will enable us to create a value network,a cash-flow for the upcoming semester and to develop a business plan.

Stakeholder analysis

A stakeholder analysis of an issue consists of finding the equilibrium of different demands from different perspectives. The stakeholder map shows which stakeholders will be considered, moreover, it will help to identify the interests and mechanisms of the stakeholders to influence other stakeholders, key people, competitors and to reduce potential risk. The center is the online platform in the center which connects every stakeholder and provides access to the AI assistant. The main target group is the users, described above. With the marketing, the first targeting would reach the bigger associations, companies or universities, furthermore individuals who personally are interested. The idea is to include specialists, such as dietitians, psychologists and personal trainers. The data analysts are mainly members in our current team who could work in collaboration with the specialist as an exchange in knowledge and expertise. Our team would provide referencing to the specialists by our customers in case the users wish to use the help of physical specialists, e.g. receive a proper diet plan from a dietitian, set a bigger goal for weight loss with a personal trainer or get better help from a psychologist. The help of the specialists could be included in the watch.

The collaboration with bigger companies e.g. IT companies where the employees are required to work long with a busy schedule or to do night-shifts would give an opportunity to help to maintain health goals for the employees. Universities could also profit from the services and help the well-being of their students and employees.

Stakeholder map.png

Competitor analysis

  • Still in progress

In conclusion, most AI assistants are used for marketing strategies or for analytical tasks. The complexity of the health and well-being related software is very low. The AI Assistants are mainly used for asking repetitive questions in the form of daily reports and to do generic measurements with Smart bracelets or watches. Compared to these devices, we concluded that our AI would bring new functionalities to the market. In reflection to other AI assistants, Motus uses its personalized questionnaire during the set-up of the software and based on that analysis the performance of the user, gives feedback and guidance with subjective questions during the day. Our team targeted four perspectives and decided to focus on well-being, healthy eating, sports activity and sleeping.

The main functionality that made our product outstanding is the use of the self-reflection technique on the users. After doing market research and conducting questionnaires, we realized the main goal is the increase of motivation. In order to accomplish this, the users reflect on their own performance and answer their own questions such as why they make specific decisions, how their decisions influence them and how they can use their knowledge for their own benefit. During the market research, one of the obvious realizations was that people lose interest just by seeing notifications or comparisons to other users.

Therefore, our product would rely on incorporating strategies from cognitive-behavioural therapy, accepting and committing therapy, mindfulness, and other science-based approaches. This could be the key element to increase intrinsic motivation. The Youper (https://play.google.com/store/apps/details?id=br.com.youper&hl=en_IN) named software stands the closest to our idea, but it focuses on the stress and mindfulness perspectives.

Compared to Youper we would add sport performance measurements and diet helps. As seen in the stakeholder analysis, we would involve additional specialists in the project which would give more opportunities and better reliability. Additionally, Wysa, the interactive penguin assistant provides similar services [29]. The penguin assistant has a highly developed AI system for any kind of mental therapy, although the software deals only with mental well-being. With Motus we tried to combine all the options together and create something that deals with not only mental well-being but also with the physical state.

Business plan and development

  • Cash-flow
  • Business model

Software milestones

1 Startup

  • Research what technologies are feasible to implement, and potentially applicable to reach the requirements
  • Division in modules with (relatively) independent functions that can be developed and tested independently
  • Create specific requirements for different modules
  • Create designs for various modules
  • Define interfaces between modules


2 Rough Scattered Prototype

  • A functional yet limited user interface is present, without taking into account human factors design.
  • Working content-generating networks have been implemented, but the output does not yet need to be fine-tuned or adaptive to input


3 Connected Prototype

  • A basic database is operational
  • The interface can accept inputs, pass the input information to the database. Human factor design will be implemented into the interface.
  • The decoder can read the database and generate output in the UI. The output does not yet need to be perfectly adjusted to the database’s content.
  • The Question-generator can generate output to be displayed in the UI


4 Rough Complete Prototype

  • Users are able to select which variables will be logged.
  • Users are able to delete data
  • Users can request transparency of the data gathered (whether this is just a textual description, direct access to the database, or otherwise)
  • The Question-generator produces human-like questions that align with the input fields
  • The decoder produces outputs that correctly reflect nontrivial information in the database.

Literature Review (separate file)

Due to bugs in the installation of the LaTeX engine of the wiki, mathematical expressions cannot be shown here. See the following Overleaf file for the literature review, and references: Literature review Overleaf file.

Overview

Work-in-progress-page

See the page WIP group 8 for an actively edited file of notes.

User guide

TODO...

Software documentation

TODO...

References

  1. Hammond, C., Qualter, P., Victor, C., & Barretto, M. (2018). Who feels lonely? The results of the world's largest loneliness study.
  2. Baumeister, R. F. & Bushman, B. J. (2008), Social Psychology and Human Nature. 2nd edition, Wadsworth, Cengage Learning, 436-441
  3. Cacioppo, J. T., & Cacioppo, S. (2018). The growing problem of loneliness. Lancet, 391, 426. https://doi.org/10.1016/S0140-6736(18)30142-9
  4. Luhmann, M., & Hawkley, L. C. (2016). Age differences in loneliness from late adolescence to oldest old age. Developmental psychology, 52(6), 943.
  5. National Academies of Sciences, Engineering, and Medicine. (2020). Social isolation and loneliness in older adults: Opportunities for the health care system. National Academies Press.
  6. https://www.government.nl/topics/coronavirus-covid-19/tackling-new-coronavirus-in-the-netherlands
  7. Chen P, Mao L, Nassis GP, Harmer P, Ainsworth BE, Fuzhong L. Coronavirus disease (COVID-19): The need to maintain regular physical activity taking precautions. J Sport Health Sci 2020;9(2):103–4. doi: 10.1016/j. jshs.2020.02.001.
  8. Lee, J. D., Wickens, C. D., Liu, Y., & Boyle, L. N. (2017). Designing for people: An introduction to human factors engineering. CreateSpace.
  9. Buehler, R., Griffin, D., & Peetz, J. (2010). The planning fallacy: Cognitive, motivational, and social origins. In Advances in experimental social psychology (Vol. 43, pp. 1-62). Academic Press.
  10. T. W. Bickmore and R. W. Picard, "Establishing and maintaining long-term human-computer relationships," ACM Trans. Comput.-Human Interaction, vol. 12, no. 2, pp. 293–327, Jun. 2005.
  11. C. D. Kidd and C. Breazeal, "Robots at home: Understanding long-term human-robot interaction," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Sep. 2008, pp. 3230–3235.
  12. https://www.wysa.io/
  13. Fasola, J., & Mataric, M. J. (2012). Using socially assistive human-robot interaction to motivate physical exercise for older adults. Proceedings of the IEEE, 100(8), 2512-2526.
  14. R. A. Dienstbier and G. K. Leak, "Effects of monetary reward on maintenance of weight loss: An extension of the overjustification effect," presented at the Amer. Psychol. Assoc. Conv., Washington, DC, 1976.
  15. R. S. Weinberg and J. Ragan, "Effects of competition, success/failure, and sex on intrinsic motivation,[ Res. Quart., vol. 50, pp. 503–510, 1979
  16. Tauer, J. M., & Harackiewicz, J. M. (2004). The effects of cooperation and competition on intrinsic motivation and performance. Journal of personality and social psychology, 86(6), 849.
  17. Frederick-Recascino, C. M., & Schuster-Smith, H. (2003). Competition and intrinsic motivation in physical activity: A comparison of two groups. Journal of sport behaviour, 26(3), 240-254.
  18. R. J. Vallerand and G. Reid, "On the causal effects of perceived competence on intrinsic motivation: A test of cognitive evaluation theory,[ J. Sport Psychol., vol. 6, pp. 94–102, 1984.
  19. R. J. Vallerand, "Effect of differential amounts of positive verbal feedback on the intrinsic motivation of male hockey players," J. Sport Psychol., vol. 5, pp. 100–107, 1983.
  20. C. D. Fisher, BThe effects of personal control, competence, and extrinsic reward systems on intrinsic motivation,[ Organizational Behav. Human Performance, vol. 21, pp. 273–288, 1978.
  21. Guthrie, S. E. (1997). Anthropomorphism: A definition and a theory.
  22. Breazeal, C., Dautenhahn, K., Kanda, T.: Social robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 1935–1972. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32552-1_72
  23. Dale, R., The return of the chatbots. Nat. Lang. Eng. 22(5):811– 817, 2016. https://doi.org/10.1017/S1351324916000243
  24. Pereira, J., & Díaz, Ó. (2019). Using health chatbots for behaviour change: A mapping Study. Journal of medical systems, 43(5), 135.
  25. Pereira, J., & Díaz, Ó. (2019). Using health chatbots for behavior change: A mapping Study. Journal of medical systems, 43(5), 135.
  26. ***
  27. Hipp, Wyrick & Company, Inc. SQLite Version 3.34.1 (2021-01-20). Retrieved 04-03-2021. URL: [1]
  28. Patel V, Flisher AJ, Hetrick S, McGorry P. Mental health of young people: a global public-health challenge. Lancet. 2007;369:1302–1313. doi:10.1016/S0140 -.6736(07)60368-7
  29. https://www.wysa.io/