document

~ Maarten Geraets / Elwin Lee / Mark Thielen / Bart Wolfs / June 2009 ~

Supporting Sportmanschip

Block 2.2

Maarten GeraetsElwin LeeMark ThielenBart Wolfs

Coach: Emilia Barakova

TU/eIndustrial DesignSocial Robots & Humanoids

June 2009

EMOTION

MOTION

Supporting

Sportsmanschip

Supporting Sportsmanschip B2.2

Foreword

In order to complete this report we would like to thank our coach dr.ir. E.I. (Emilia) Barakova for supporting us through the project. She assisted us when we got stuck and brought us in contact with experts. Also expert R. van Berkel assisted us to setup and ana-lyze the movements of the user test. In addition we would like to thank dr. T. Lourens for his data-analyzing, without his support we were not able to analyze the data on a significant way. Fur-thermore we would like to thank Dr. J. Terken for his assistance of evaluating the data of the user test. In the end we would like to thank S. Meulendijk from Zylom and fellow-students for their as-sistance of game-development during the report.

Abstract

This project report is about the Supporting Sportsmanschip project. This project was completed by four B2.2 students; Maarten Geraets, Elwin Lee, Mark Thielen, Bart Wolfs. The focus of this project was to design a training robot for sportsman that not only trains your skills, but also motivates you, by for instance giving feedback on your movement and emotions. Human emo-tions are, apart from other ways, expressed by our way of moving. In the end we came up with a chair which adjusts the game-en-vironment based on the movements of the gamer. The chair con-tains distance sensors which are connected with the computer. Furthermore the computer analyses the mouse-movements. The computers analyses the data in order to see whether the gamer is conceiving the game. When this isn’t the case, the computer will change the game-environment in such a way it catches the gamer’s attention. In the end this will result in a more vivid game-interaction and longer game-entertainment.

4 Supporting Sportsmanschip B2.2

Foreword 3

Abstract 3

Table of Contents 5

Introduction 6

Approach 7 Starting Point 8 Sport Choice 8

Research 9 Movement LABAN-characteristics 9

User Test 11 LABAN Analysis Tool 12 Russell’s Circumplex Model Analysis 16 Neural Network Analysis 19 Neural Gas Network 21 Amplitude & Frequency Analysis 23 Overall Analysis Conclusion 25 Final Conclusion User Test 26

Reconsideration of Sports-choice 27

Idea Generation 28 Heartbeat Timer 28 Game environment morphing 29 Boredom measurer 30

Idea Selection 30

Concept development 31

Initial Concept 32

Game 33

Concept testing 34 Engagement Level of Body Posture 35 Results sensor measurement 38

Concept conclusion 39

Future development 39

References 40

Appendix 41

Table of Contents

Introduction

This project is about designing a training robot for sports-man that not only trains your skills, but also motivates you, by for instance giving feedback on your movement and emotions. Human emotions are, apart from other ways, ex-pressed by our way of moving. Recent research has proved that it is possible to track the emotional characteristics of human movement. The design outcome of this project has to be based on this theory.

Approach

7~ Maarten Geraets / Elwin Lee / Mark Thielen / Bart Wolfs / June 2009 ~


Starting Point

Before we made a start with the project we had to redefine the project direction. Our coach addressed to perform a user test using a Wii-Mote as starting point. Therefore we had to setup a test which let the user performing a simple movement using the Wii-Mote, by collecting the data of the acceleration-sensors in the Wii-Mote we could analyse the movement. In our case we decided to focus on a simple wave-movement, because it is usually performed in a 2D space and was confirmed to be suitable for this testing-purpose. Afterwards we could make connections with the emotional state in which the move-ments were performed. Cornelia Petrutiu made an MATHLAB application which should be able to analyse the data in such a way that it could determine the original emotional state in which the movement was performed. Possible distinguishes are sad, happy, angry, nervous and afraid. More information about this phase in the design cycle will be explained later on.

Sport Choice

In order to complete the report we had to choose a sport to focus on. We decided to make a list of interesting sports concerning this project. Afterwards we clustered the sports into cool and un-cool sports, concerning movement and emotions. Subsequently we selected the most interesting sport to focus on. Based on the focus of our project and the integration of emotions into the sport we decided to focus on dancing. Dancing is a sport which both includes physical movement which has a strong relation with the expressed emotions.

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Research

Movement LABAN-characteristics

As mention before we had to analyse the movements which were performed by the Wii-Mote. The MATHLAB application of Cornelia Petrutiu de-scribed before made use of the LABAN characteristics to distinguish the emotions [Apendix A]. The LABAN characteristics provide a founda-tion to understand, observe, describe and notate all forms of human movement. It provides descriptors for the content of human body movements:

• Body:partsofthebodythatareused• Space:pathsofamovement• Shape:thechangingformsthatabodymakeswhenmoving• Effort:howthebodyusesitsenergywhileperformingmovements• Relationship:interactionswithothers

According to Petrutiu’s report it is important to focus on the Effort. Laban’s Effort is considered to be the quality of move-ment which is most relevant for recognizing emotion in movement: The Effort present in a movement, together with its varia-tions during the movement, conveys the expressive information necessary for understanding emotion characteristics from movement.


Supporting Sportsmanschip B2.2

User Test


LABAN Analysis Tool

Goal

To test the accuracy of the Wii Remote-application in measuring the mind set of movements according to LABAN movement analysis and to see if there’s a correlation between emotions and (waving) movement.

Means

A test setup which consists of a series of 5 videos [Appendix B] to bring our test persons in a preferred mind set. A test person waves after every movie fragment and states the current mind set on the scales related to emotions. The data output of the waves provided by the LABAN analysis tool is compared with the data output from the wave produced in a neutral mind state. The differences the neutral and a specific wave are analyzed to see if there’s a cor-relation with the intended mind set. Approximately 30 participants are used to get quantifiable results. User test was conducted on 2nd, 5th and 6th of March 2009.


Figure 1.Topview User Test Setup

Using Darwi in Remote we captured the move-ment data from the wi imote.

One laptop was used to provide movies and audio to the users.

The observers were posit ioned behind the users to not interfere with their focus during the user test.

U s e r T e s t S e t u p


User Test in action

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Conclusion - User Test Analysis

To conclude the user test the correlation between the written, visual and movement emotion was checked. The way this was done by connecting the es-sential emotion of the video with the written emotion and connection to their waving movement.

Alas, as the three dimensions of LABAN seemed a too complex to use in this user setup to concretely connect emotions to the movement. However what people indicated on the emotion list seemed to connect to what the movies were intended to evoke [Appendix C & D] and [5].

Movie 1 Movie 2 Movie 3 Movie 4 Movie 5

Surprise 32 2 7 12 7

Sadness 3 25 2 8 14

Happy 5 8 29 9 4

Fear 9 1 1 21 2

Anger 3 4 1 3 25



Russell’s Circumplex Model Analysis

As the manner of data analysis with this many parameters seemed too complex the decision was made to condense the data analysis in forms of Russell’s model.

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The secondary analysis using the Russell circumplex was to find out the correlation of the velocity of movement and the arousal level.Within the Russell model the placement of these two emotions:

Sadness : Low velocity – Low arousal – NegativeHappy : High velocity – High arousal – Positive

Two of the most separated emotions, Happiness and sadness, were taken to be placed within the Chi-square to determine the probability of deviation in the given data. The tables below consist of 2 kinds of tables. All measurements mean that all the measurements from Happy and Sadness were used for analysis. And significant measurements mean that only the measurements with a minimum distance of 2 in the emotion scales were taken into the analy-sis from Happy and Sadness [Appendix E].



Also the comparison tables for the acceleration of the movement were made as well as for the variation and here are the Chi-square tables of them:

Conclusion – Russell’s Circumplex Model Analysis

To be of any significance the probability of deviation has to be < 0.05 (5%). This means that even by analyzing two parameters of the total data there is no correlation to be found between movement and “true” emotion. The conclusion can be made from these Chi-square tables is that there is a closer connec-tion between movement and emotion in velocity then in any of the other factors.

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Neural Network Analysis

A 3rd analysis has been conducted to see if the waving movements of a particular emotion can be clustered in classes to see if there’s a general waving pattern for that emotion.The x- acceleration values (horizontal movement) from the waving data is converted in such a way that it gives a covering graph of all the maximum values. This is done for all the waving movements of the 33 users from the user test. Faulty measurements, e.g. measurements being too short or measured incor-rectly, weren’t used for this analysis.

See example below:



Next the data was resampled into data with consistent time intervals, since the WiiMote captures the acceleration values in inconsistent time intervals. The used time vector was from 10 to 160 with a step of 10 resulting in 160/10=15 resampled coordinates. The choice of starting from time=10 instead of time=0 is that it will act as a buffer when a peak value doesn’t start at the beginning resulting in negative values. Some movements were not suitable for the resampling because the graphs of the movement were too short (smaller than 160) or faulty measurement resulting in negative values.


Neural Gas Network

The data is run through a neural gas network program in MATLAB. The unsupervised learning here is to self-organize a feature map with no clear output answer. The program organizes input data by comparing it to the reference data. Each emotion is run through this program. The clustering of the move-ments is performed with classes from 2 to 10 to see if there’s a difference using different amount of classes. The emotions with the most significant results from clustering are shown below:

Sadness

For the Sadness emotion there is a distribution of users with the clustering. The small majority in a particular class are from the total classes of 2 until 4. The percentages there are approximately 50%. Clustering with classes above 4 will result in a quite equally divided clustering, meaning there’s no distinc-tion when defining the movements in that amount of classes.


Looking at the emotion Happy it is hard to say if there’s a general moving pattern for this emotion, since most of the users were somewhat equally divided among the classes. There are several clustering classes (clustering 2, 3, 4 & 6) that show a higher clustering number in a particular class compared to other classes in the same clustering of classes. But the percentage of the division is only approximately 30% to 50% among a class.

Conclusion – Neural Network Analysis

The emotions Sadness and Happy seem to be the ones to be the most capable of finding a general waving pattern according to this analysis. It isn’t pos-sible to make a final conclusion because the percentage (approximately 50%) isn’t high enough to say that there’s a general moving pattern for a particular emotion. There’s also a possibility that the general movement consists of a combination of 2 or more waving pattern that fits with a particular emotion. But this can’t be concluded from this analysis.

Happy

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Amplitude & Frequency Analysis

A 4th analysis was conducted by looking at the amplitude and frequency of the movements. The amplitude stands for the average acceleration and the frequency stands for the number of waves per second. The program to convert all the waving data of the users into an amplitude-frequency graph has been made Dr. T. Lourens from the department Designed Intelligence at the faculty Industrial Design. The 1st graph showing all the movements of the emotions plotted on the graph by color. Here it is possible to see some kind of clustering of an emotion on the graph.

~ Maarten Geraets / Elwin Lee / Mark Thielen / Bart Wolfs / June 2009 ~~ Maarten Geraets / Elwin Lee / Mark Thielen / Bart Wolfs / June 2009 ~


The 2nd graph shows the mean and the median of the movements of the emotions. The mean is calculated by taking the average of the amplitude and frequency of all the movements of a particular emotion. The median is calculated by taking only the value of the movement that is located in the middle of the total amount of movements. If total of the amount of movements has an even number of movements the average is taken of the movements are located in the middle.

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Conclusion – Amplitude & Frequency Analysis

A clear conclusion cannot be made that there’s a clear distinction between movements according to a particular emotion and the amplitude and frequency of a waving movement. By looking at the first graph it’s visible that there are no clear separate clusters of movements for an emotion. When looking at the movements of the emotion Happy, they tend to be positioned in an area somewhat close together compared to other emotions. The second graph shows that the movements of Happy, Surprise and Sadness tend to be the best emotions to see any distinction between them. The remaining emotions, Fear and Anger, are in between. What is remarkable is that for the emotion Happy the waving movements are low in amplitude and frequency and for the emotion Sadness the movements are higher in amplitude and high in frequency. The emotion Anger seems to be deviating as seem that it’s almost similar to the waving behavior of the Neutral. The reasons for this are probably caused by the measurements during the user test.

Overall Analysis Conclusion

Conclusion - User Test AnalysisFrom the user test the conclusion can be made that there’s no concrete connection between emotions and the waving movement, due to the complexity of the three dimensions of LABAN. The total score of each emotion scored in either 6 or 8 of the LABAN table. However a conclusion can be made from the user test is that the entered data on the emotion scales seemed to connect to the intended emotion of the movies.

Conclusion – Russell’s Circumplex Model AnalysisIn the Russell’s circumplex model analysis the waving data of Happy and Sadness were analyzed using the velocity, acceleration and variation. To be of any significance the probability of deviation has to be < 0.05 (5%), which was not the case. There seemed to be no correlation between the movement and the “true” emotion using the Russell’s circumplex model.

Conclusion – Neural Network AnalysisThe neural network analysis shows that there’s no general waving pattern for an emotion. The emotions Happy and Sadness had the highest probability (approximately 50%), but this isn’t sufficient enough to conclude that there’s a general waving pattern.

Conclusion – Amplitude & Frequency AnalysisThere’s no clear distinction between movements of an emotion in its amplitude and frequency. The clusters seem to be overlapping over each other for a large area of an emotion. When looking at the median of each emotion, it seems that Happy and Sadness/Surprise are the emotions which are the most opposite from each other.



Final Conclusion User Test

Looking back at the user test there seemed to be a lot of uncertainties and influenceable factors that play a role in the analyses. First of all the program for the LABAN analysis wasn’t tested yet, which was our objective of this project to test whether the program worked or not. Secondly the user test itself had factors that could influences the analyses. There’s a probability that the movies weren’t influencing the user enough, also the sequence of showing movies after each other could influence the users emotional state for each movie. There’s also the probability of faulty measurements. The users could be holding the WiiMote in such a way that it would give different output signals. Some users didn’t conduct the user test seriously resulting also in deviating measurements. Lastly there’s a probability that there’s no correlation between emotions and waving or at least not a clear distinction.

What we can conclude and use from our analyses is that Happy seems to be the best emotion to work with. From all the analyses the emotion Happy scored overall the “best” followed by the emotion Sadness. After a discussion with Dr. T. Lourens, from the department Designed Intelligence, the suggestion was to focus on looking at the amplitude and frequency of a movement. This is a more solid method of looking at the distinction between different movements.

This choice is then made to use Happy and Sadness in this project, because those are the emotions that are with the lowest and highest amplitude/fre-quency and can be seen as the opposites of each other.

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Reconsideration of Sports-choice

After performing the user test with the Wii-Mote we brainstormed about possible integration of this into the dancing scene. This resulted into multiple concepts, like an adaptive-dance floor, adaptive audio-controller and adaptive projections on the background based on the emotional state or expression of the dancer/artist.

In order to develop these concepts out, we searched for experts from the dance-academy. R. van Berkel assisted us and brought us in contact with experts in the dance-scene. However they replayed not in time and the project-deadlines were near. Furthermore we had no experience in the dancing scene at all, so we really needed some external input. Therefore we decided to reconsider the sport-choice. In the end we decided to focus on e-sports/gaming. Our reasoning therefore was that we are able to contact experts in this area quickly and we had some experience in designing games ourselves. Furthermore the gaming-industry is a hot issue and developing nowadays. Furthermore in the future, electronically ways of entertainment will become more important.



Idea Generation

After the refinement of the sports-direction of the project we brainstormed about ideas concerning, movement, (computer) games and emotions. We came up with a few concepts which will be addressed below.

Heartbeat TimerThe heartbeat timer is based on the fact that arcade-games usually use a count-down timer. The heartbeat timer uses the gamers’ heartbeat as countdown. This means when the gamer is stressed, the heart rate increases which result in less game-time. So in the end the assignment for the gamer is to keep his or her nerves and heart rate in control in order to increase game-time. This will eventually result in more game-time and a higher score. You can see an illustration below.

Heartbeats left:

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Stress-high heartrate-less time

No Stress-low heartrate-more time

Game time left, based on remaining heartbearts

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Game environment morphing

The game environment morphing concept is basically and game-environment which changes based on the emotion-state of the gamer. For instance, when the gamer is very aggressive the game-environment becomes darker. Possible variables which can be changed within the game are: players, characters, sounds, game play/speed, goal/mission an experience. On the other hand, when the gamer is more relaxed, it will result in a more relaxing game-environ-ment (bright sun, flowers, happy people etc.). In the end this will result in a more vivid game-experience.


Agressive gamer

Relaxed gamer

Agressive game

Relaxed game

Possible game changes:

-environment-players-characters-sounds-gameplay/speed-goal/mission-experience


Boredom measurer

The boredom measurer measures the level of player’s boredom. When the system notifies the gamer’s attitude towards the game is lacking or the gamer is becoming bored of the game, it will change game-environments, such as the gaming-speed, mission and environment in such a way it keeps the gamer excited.

Gamer bored Gamer more excited and happy

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Idea Selection

Eventually we decided to combine these concepts into one as described below. We used the boredom measurer as starting point. Furthermore we contact-ed Zylom, an online-gaming company in Eindhoven. We talked with Sebastiaan Meulendijk, game-designer at Zylom. He addressed that in-game changes based on the emotional-state of the gamer is a hot-issue and has a high potential as product. According to Meulendijk the big game-companies are also working on these issues right now. Furthermore it will keep the gamers attention and will eventually increase the entertainment. Furthermore was this con-cept, contradictory to the concepts from the dance-experience-brainstorm, realizable on short time. The final concept will be discussed in the next chapter.

Concept development



Initial Concept

WhyThe idea of making an adaptive game is to create a longer game-play (du-ration of play) to games which usually have fixed play sequences. For ex-ample: level1, boss, level2, boss, are a fixed set of sequences. All of these sequences have a set number of achievements which have to be fulfilled before the next sequence will be activated. This can be done in form of col-lecting a number of items, completing a puzzle or destroying a number of enemies. The problem with this however is that the fixed time and playabil-ity of these sequences. If a player likes or dislikes a certain part within the game it still will remain the same, while actually he would rather have the things he likes last longer and those which are disliked be shorter.

A m p l i t u d e

F r e q u e n c y

HowThe concept is to create a programmed logarithm to detect emotions from players from their mouse movement. What it does is calculate the average frequency and amplitude of the move-ment to determine whether the player is engaged in the game or not. The average values to imple-ment within the program were taken from the user test of the waving movement. After this in the game pong the logarithm was programmed to adapt the game while playing. See appendix A for the source code.

WhatWithin the game there are certain adaptive aspects which were applied to create its dynamics. If the player would be registered as being bored of the game, the ball speed will be upgraded. If the player is registered as being stressed, the breadth of his or her bat will become higher. I over time a player has a lower or higher than average velocity the levels of the program registering stress or boredom will be adapted accordingly. Also the intelligence of the enemy is adapting according to the scores of both parties. If the enemy has a score which is five points higher than the player has the difficulty will be turned down and vice versa.

Game

For the concept we decided to use a basic Ping Pong game. It is easy to understand and to play. Furthermore it is easy to adapt different parameters in order to in- or de-crease the game-play experience, like ball speed, supidity of the computer or movement of the players. Besides it was easy to design in Flash, so we were able to connect the game with Phidgets, including suitable sensors. You can see the source code in Appendix F. You can play the basic game in Appendix G and the adapive game in Appendix H. You can see a picture of the Ping Pong game below:



Concept testing

Below you can see the results of the basic Ping Pong game (untouched game) and the Adaptive Ping Pong game (including additional programming). For this test we decided to split up the user test into two groups. One group (four persons) will be testing the original, untouched basic game. The other group (four people) played the Adaptive game. We asked all the players to play the the game untill they are bored. Below you can see the results of these tests:

As you can see, the averages of the Adaptive, intelligent, Ping Pong game is around 1,5 minute higher than the original game. Hereby is proved than this game keeps the attention of the gamer longer.

Basic Game Adaptive GamePlayer 1 4,49 Player 5 7,07Player 2 3,33 Player 6 5,03Player 3 1,57 Player 7 4,12Player 4 2,02 Player 8 2,4

Average 3,05 Average 4,45

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Engagement Level of Body Posture

From the user test analyses it could be concluded that the measuring of emotions in movement seemed to be difficult. The happy emotion seemed to be the emotion with the highest validation probability. The choice was made to construct a videogame where human emotions (extracted from the mouse movement) will be used as input for adapting the game. A meeting was held with dr. J.M.B. Terken, from the department User Centered Engineering, to gain more knowledge about extracting the emotional movement data from the movement made with the mouse. The expert stressed that it would be dif-ficult to extract the emotional data because the mouse movement intended for the game plays a crucial role in the analysis. The suggestion was to look at the engagement level of a human instead of the level of happiness. The analysis of the engagement level of human beings was conducted in by Mota S. & Picard R.W., using a chair consisting of pressure sensors to determine the body postures.The ability to sense the engagement level by measuring body postures was implemented in the adaptive gaming concept. Changing the concept into the adaption of the game according to a user’s input videogame. The engagement level versus the body posture that will be used in the game concept can be stated as:


• Highengagement–Sittingonedgeofthechair.• Normalengagement–leaningforwardfromanormalsittingposition.• Lowerengagement–Leaningbackwardfromanormalsittingposition.• Lowengagement–Slumpingback.


The high and normal engagement body postures were derived from an experiment conducted by Mehrabian and Friar [2]. In the experiment the subjects were asked to adopt positions, while seated, that they would employ when having a conversation with someone. The experiment revealed a result that the subjects took a leaning forward or a decrease of leaning backward to indicate a positive attitude. A research that accompanies the high and normal engagements was conducted by D’Mello, Chipman and Graesser [3]. Their research was to automatically monitor a student’s posture to track the affective states of boredom (low engagement) and flow (high engagement) by using a chair equipped with pressure sensors. The results showed that flow was ac-companied by an increase of pressure on the seat of the chair. In contrast, boredom indicated an increase of pressure on the back of the chair, which is used in the lower and low engagements in our concept.

Another aspect which could be relevant to determine the engagement level is frequency of the movement of the body. The research of D’Mello, Chipman and Graesser [3] also indicated that boredom is typically accompanied by an increase of activity on the seat of the chair; e.g. fidgeting. In addition an ex-periment conducted by Bianchi-Berthouze N., Cairns P., Cox A., Jennett C. & Kim W. W. Kim [4] experimented by using 2 groups playing 2 different games; a simple click game and a shooting game. The “clicking” group could be characterized by frequent changes in the sitting position, alternating between a very relaxed position and a very attentive one. The “shooting” group revealed a different pattern with very few changes in body posture. The results from these papers the frequency of body movement can be stated versus the engagement level:

• Lowfrequencyinbodymovement–High/Normalengagement.• Highfrequencyinbodymovement–Lower/Lowengagement.

With the known body postures the next step is to build or find a chair consisting of sensors to measure these postures somewhat similar to the chair stated in the paper of Selene Mota. Master student Rick van de Westelaken made chair consisting of pressure pads integrated in the seating. This chair was made available to the project with the approval of the master student. New pressure sensors had to be purchased, as almost all the pressure sensors were miss-ing. Implementing and testing the sensors when a user is sitting on it seemed problematical. The pressure sensors were not accurate enough to measure the exact positioning of the body. Weight of the body was difficult to distribute evenly of over the pressure pads, leading to an accurate measurement of the body posture.

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The choice was made to discard the use of pressure sensors and implement small distance sensors inside the chair. The distance sensors will be placed at the top and bottom of the back of the chair. The state of body postures can found by measuring the distance between the sensor and the user sitting in a chair. A Phidgets set will be used to transfer the sensor data to the computer and the program used to process the data will be written in Flash.


Based on the figure on the right the right body postures could be analyzed with the sensors inputs. The visual model consists of two lines representing the upper legs and back of a person. The upper sensor input controls the rotation of the back and the bottom sensor the x position of the body related to the chair. The quite representative output of the body movement that the figure shows makes the interpretations quite reliable. This means the sensing of the body positioning has made a good step in the right direction for now [Appendix I].


Results sensor measurement

Above you can see the sensor values based on the positioning of the gamer in the chair.


Concept conclusion

The most important challenge in this project was to use movement as a means to understand emotional behavior. After concluding that the Laban move-ment analysis didn’t give us reliable results. Analyzing the data using frequency and amplitude did result in usable criteria. The intelligence that can be achieved through being aware of emotional states gave us opportunities to improve a sport. Gaming turned out to be an interesting field for us to design in when dance didn’t seem to inspire us to come up with valuable and new concepts. The experience with gaming and research into this field did enable us to see and create valuable changes into the experience of gaming. The concept of intelligent adaptive gaming suited well for the measurement of frequency and amplitude in mouse movement. Experimenting with the well-known Pong game our test results have shown that there is potential adding intelligence into gaming.

Future development

A next step in the development of this concept would be to test the integration of the both sensing methods. A step that is left in programming the distance sensors for the body posture measurement is to use the frequency of changing body postures over time. As said before a somewhat fixed body position over time also gives clear cues to an engaged experience. More elaborate tests could indicate the quality of both sensing methods and conclude on a pos-sibly strong combination of the two. Taking this project on a higher level in another semester 2nd year project we recommend reconsidering doing a user test using the frequency and ampli-tude movement analysis. We are very convinced that emotion extracted from this analysis can contribute to the field of adaptive gaming. So the emphasis of the project can lay on the validation of the measurement as it has been in this project.


References

Coach

- dr.ir. E.I. Barakova, Faculty Industrial Design, Department Designed Intelligence, University of Technology Eindhoven

Experts

-R.E.A. van Berkel, Faculty Industrial Design, Education Department Industrial Design, University of Technology Eindhoven-dr.T Lourens, Faculty Industrial Design, Department Designed Intelligence, University of Technology Eindhoven-Dr. J.M.B. Terken, Faculty Industrial Design, Department User Centered Engineering, University of Technology Eindhoven-S. Meulendijk, Game Designer, Zylom-C.S. Petrutiu, Faculty Mathematics and Computer Science, University of Technology Eindhoven

Students

-R. van de Westelaken, Master Student, Faculty Industrial Design, University of Technology Eindhoven-T.E.L.N. Frissen, Master Student, Faculty Industrial Design, University of Technology Eindhoven

Papers

[1] Mota S. & Picard R.W. (2002). Automated Posture Analysis for detecting Learner’s Active State.[2] Mehrabian A., & Friar J.T. (1969). Encoding of attitude by a seated communicator via posture and position cues, Journal of Consulting and Clinical Psy-chology, vol. 5.[3] D’Mello S.K., Chipman P., & Graesser A.C. (2007). Posture as a predictor of learner’s affective engagement.[4] Bianchi-Berthouze N., Cairns P., Cox A., Jennett C. & Kim W. W. Kim (2006). On posture as a modality for expressing and recognizing emotions.[5] Rottenberg, J., & Ray, R. D., & Gross, J. J. (in press). Emotion elicitation using films. To appear in J. A. Coan & J. J. B. Allen (Eds.), The handbook of emotion elicitation and assessment. London: Oxford University Press.


Appendix

document

Documents

game environment

sistance of game

abstractthis project

concept conclusion

concept development

longer gameentertainment

vivid gameinteraction

neural network analysis