Updating!!
Something else.
=== Thesis
Acknowledgments 50%
Abstract 10%
Chapter 1 Introduction
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20%
1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 2 Related Work
2.1 View from Psychology . . . . . . . . . . . . . . . . . . . . . . . . . .100%
2.1.1 Emotion Models . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.2 Characteristic and Roles of emotion . . . . . . . . . . . . . . .
2.2 Recognition in Artificial Intelligent . . . . . . . . . . . . . . . . . . . 100%
2.2.1 Emotion Recognition . . . . . . . . . . . . . . . . . . . . . . . 90%
2.2.2 Learning Method . . . . . . . . . . . . . . . . . . . . . . . . . 0%
2.3 Relevance of Emotion Research for Affective Computing . . . . . . .
2.3.1 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.2 Health Care . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.3 Tutor and Education System . . . . . . . . . . . . . . . . . . .
Chapter 3 Methodology
3.1 Emotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Learning Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 4 Experiment
4.1 Mood Induction and Data Collection . . . . . . . . . . . . . . . . . .
4.2 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 5 Conclusion
Appendix A Questionnaire for Mood Induction 5%
Sunday, March 25, 2007
Friday, March 23, 2007
如何使用 Win32 API 存取 RS232
http://www.csie.ntu.edu.tw/~b88032/expr/AcceRS232.htm
This is a reference link to tell me how to use Win32 API to access RS232.
A clearly and easy one.
Tuesday, March 06, 2007
[Progress Report] 2007 Feb
- What I have done in February
- 在二月當中，在春假之前，我收集了共15人的資料。其中四種情緒都可以使用的約有12個人
- 已經把取出關鍵時刻資料的parser寫好了. 比如做happy的狀態, 我只需要取所有collect的過程中的最後三分鐘的資料, 而現在parser會根據log的狀態去做.
- 跟潘昭將我們的實驗過程大致上做了一個初版的影片. 其中包含如何進行實驗, 以及一個簡單的情境說明我們做的事情有何功效.
- What is the main research issue that I am working on right now
- 將data轉化為feature
- related work的整理與撰寫
- What I plan to do in March broken down into weekly goals
- now ~ Mar. 10: 寫完related work, 準備group meeting報告
- Mar. 11 ~ Mar. 17: Feature parser. 準備care robot demo.(要demo啥??!!)
- Mar. 18 ~ Mar. 24: Learning, Mining, (decision tree以及SVM開始) care robot demo.
- Mar. 25 ~ Mar. 31: 利用sy的sensor收集data?(需再討論), 寫一部份論文關於實驗的部份
加油畢業!!
Tuesday, February 13, 2007
Friday, January 26, 2007
提醒事項
combime parser
第一個參數：資料夾名稱
會自動把讀入的四個channel變成一個檔案myFile.cvs
log parser
第一個參數：資料夾的名稱
會根據log.log把每一段的檔案切出來(fear.cvs, happy.cvs, peace0.cvs, etc.)
feature
第一個參數：資料夾的名稱
會計算features，放到每一段的檔案中(same as name in log parser)
merge to one for support vector machine
把所有資料夾中的所以段結果放到一個大的檔案中給svn使用
第一個參數：資料夾名稱
會自動把讀入的四個channel變成一個檔案myFile.cvs
log parser
第一個參數：資料夾的名稱
會根據log.log把每一段的檔案切出來(fear.cvs, happy.cvs, peace0.cvs, etc.)
feature
第一個參數：資料夾的名稱
會計算features，放到每一段的檔案中(same as name in log parser)
merge to one for support vector machine
把所有資料夾中的所以段結果放到一個大的檔案中給svn使用
Thursday, January 18, 2007
LED燈接法
LED要串一個電阻
假設LED的規格是x V, 工作店最大事 y mA
則串接的電阻的電阻值為 （電源電壓 - x)/z, 其中z <= y
一般來說z=0.01
因此紅色1.8V的LED要串大概330歐姆的電阻
電阻的串法
LED長腳接五伏電壓
短腳接電阻
電阻的另一個腳接地
圖晚點補
假設LED的規格是x V, 工作店最大事 y mA
則串接的電阻的電阻值為 （電源電壓 - x)/z, 其中z <= y
一般來說z=0.01
因此紅色1.8V的LED要串大概330歐姆的電阻
電阻的串法
LED長腳接五伏電壓
短腳接電阻
電阻的另一個腳接地
圖晚點補
Tuesday, January 09, 2007
Recording Data
Today, I am trying to parse the recording data.
I count the duration of my experiment and the file. I found that they did not match!! There are 16 times than the samples I collect. I need to find out what the others stand for.
Sigh....
I count the duration of my experiment and the file. I found that they did not match!! There are 16 times than the samples I collect. I need to find out what the others stand for.
Sigh....
Tuesday, January 02, 2007
[Reading] XPod: a Human Activity Aware Learning Mobile Music Player
http://ebiquity.umbc.edu/paper/html/id/335/XPod-A-Human-Activity-Aware-Learning-Mobile-Music-Player
Sandor Dornbush, Jesse English, Tim Oates, Zary Segall, and Anupam Joshi
Jan 08, 2007
I think the work is an on-going one. In the paper the show on Jan 08, 2007 (a coming day), I think the writers take out the part of emotion, which is the one I am interested in. The try to uses different method, including decision tree, AdaBoost, SVM, KNN, neural networks, to classify 5 different state. They collect GSR, acceleration (2D), skin temperature, BVP, time, song information and beats per minute to predict how a user would rate a song in the future. They have 565 training instances. The result consider states is a little better than the one without states. More precisely speaking, states are considering the physiological information gather from the sensors. The result range is from 31.87% to 46.72, and mean square error is from 0.17 to about 0.45.
I think the result is not very well. I think they should collect more training data. Some more interesting points should be added into XPod.
Sandor Dornbush, Jesse English, Tim Oates, Zary Segall, and Anupam Joshi
Jan 08, 2007
I think the work is an on-going one. In the paper the show on Jan 08, 2007 (a coming day), I think the writers take out the part of emotion, which is the one I am interested in. The try to uses different method, including decision tree, AdaBoost, SVM, KNN, neural networks, to classify 5 different state. They collect GSR, acceleration (2D), skin temperature, BVP, time, song information and beats per minute to predict how a user would rate a song in the future. They have 565 training instances. The result consider states is a little better than the one without states. More precisely speaking, states are considering the physiological information gather from the sensors. The result range is from 31.87% to 46.72, and mean square error is from 0.17 to about 0.45.
I think the result is not very well. I think they should collect more training data. Some more interesting points should be added into XPod.
Before the holiday
What should me do .
-- problem definition
-- complete survey related work
-- solultion (optional)
-- experiment result
-- problem definition
-- complete survey related work
-- solultion (optional)
-- experiment result
Monday, January 01, 2007
[Reading] Using Human Physiology to Evaluate Subtle Expressivity of a Virtual Quizmaster in a Mathematical Game
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WGR-4F4WYNR-1&_coverDate=02%2F01%2F2005&_alid=516189552&_rdoc=1&_fmt=&_orig=search&_qd=1&_cdi=6829&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=c9c7e4550c1e39c2d84e639d3e50adcd
Helmut Prendinger and Junichiro Mori and Mitsuru Ishizuka
year 2003.
Abstraction: The aim of the experimental study described in this article is to investigate the effect of a life-like character with subtle expressivity on the affective state of users. The character acts as a quizmaster in the context of a mathematical game. This application was chosen as a simple, and for the sake of the experiment, highly controllable, instance of human–computer interfaces and software. Subtle expressivity refers to the character's affective response to the user's performance by emulating multimodal human–human communicative behavior such as different body gestures and varying linguistic style. The impact of em-pathic behavior, which is a special form of affective response, is examined by deliberately frustrating the user during the game progress. There are two novel aspects in this investigation. First, we employ an animated interface agent to address the affective state of users rather than a text-based interface, which has been used in related research. Second, while previous empirical studies rely on questionnaires to evaluate the effect of life-like characters, we utilize physiological information of users (in addition to questionnaire data) in order to precisely associate the occurrence of interface events with users’ autonomic nervous system activity. The results of our study indicate that empathic character response can significantly decrease user stress and that affective behavior may have a positive effect on users’ perception of the difficulty of a task.
Keyword: Life-like characters; Affective behavior; Empathy; Physiological user information; Evaluation
==== After Read ====
The writers use physiological signal to evaluate user interface and interaction between human and computer game. Their primary hypothesis is that if a life-like character provides affective feedback to the user, it can effectively reduce user frustration and stress. They use bio-sensors including GSR and BVP. Another short questionnaire is requested. The feature they get from the sensor signal is mean. The easy game is summing up the given five numbers. The result shows that the hypothesis is held expect the relation between game score and empathy.
The feature they get is very simple. They use a few sensors to show the result is good. I think the write wants to tell us that we can use just a few sensors the justify the helpful of well-design user interface. Some more application can be derived from the work.
Helmut Prendinger and Junichiro Mori and Mitsuru Ishizuka
year 2003.
Abstraction: The aim of the experimental study described in this article is to investigate the effect of a life-like character with subtle expressivity on the affective state of users. The character acts as a quizmaster in the context of a mathematical game. This application was chosen as a simple, and for the sake of the experiment, highly controllable, instance of human–computer interfaces and software. Subtle expressivity refers to the character's affective response to the user's performance by emulating multimodal human–human communicative behavior such as different body gestures and varying linguistic style. The impact of em-pathic behavior, which is a special form of affective response, is examined by deliberately frustrating the user during the game progress. There are two novel aspects in this investigation. First, we employ an animated interface agent to address the affective state of users rather than a text-based interface, which has been used in related research. Second, while previous empirical studies rely on questionnaires to evaluate the effect of life-like characters, we utilize physiological information of users (in addition to questionnaire data) in order to precisely associate the occurrence of interface events with users’ autonomic nervous system activity. The results of our study indicate that empathic character response can significantly decrease user stress and that affective behavior may have a positive effect on users’ perception of the difficulty of a task.
Keyword: Life-like characters; Affective behavior; Empathy; Physiological user information; Evaluation
==== After Read ====
The writers use physiological signal to evaluate user interface and interaction between human and computer game. Their primary hypothesis is that if a life-like character provides affective feedback to the user, it can effectively reduce user frustration and stress. They use bio-sensors including GSR and BVP. Another short questionnaire is requested. The feature they get from the sensor signal is mean. The easy game is summing up the given five numbers. The result shows that the hypothesis is held expect the relation between game score and empathy.
The feature they get is very simple. They use a few sensors to show the result is good. I think the write wants to tell us that we can use just a few sensors the justify the helpful of well-design user interface. Some more application can be derived from the work.
Saturday, December 30, 2006
[Reading] XPOD - A Human Activity and Emotion Awake Mobile Music Player
http://ebiquity.umbc.edu/paper/html/id/280/
==== After Reading ====
It is a prototype application. They collect acceleration, galvanic skin response(GSR), skin temperature, heart flow, and near body temperature as input data. By using fully connected neural network, the system predict whether the user will skip this song. It looks like the product recently provide by Nike and Adidas.
But I think that the claim of song decision by emotion detecting is a little weak. Mostly they did is putting the bio-sensor signals into NN. They considered the changing of the signals. They did not explain how to consider the emotion and how they inference the result.
Moreover, I think some bio signals can help the recognition of activity, especially the `actively'.
XPod - a human activity and emotion aware mobile music player
Authors: Sandor Dornbush, Kevin Fisher, Kyle McKay, Alex Prikhodko, and Zary Segall
Book Title: Proceedings of the International Conference on Mobile Technology, Applications and Systems
Date: November 17, 2005
Abstract: In this paper, we consider the notion of collecting human emotion and activity information from the user, and explore how this information could be used to improve the user experience with mobile music players. This paper proposes a mobile MP3 player, XPod, which is able to automate the process of selecting the song best suited to the emotion and the current activity of the user. The XPod concept is based on the idea of automating much of the interaction between the music player and its user. The XPod project introduces a "smart" music player that learns its user's preferences, emotions and activity, and tailors its music selections accordingly. The device is able to monitor a number of external variables to determine its user's levels of activity, motion and physical states to make an accurate model of the task its user is undertaking at the moment and predict the genre of music would be appropriate. The XPod relies on its user to train the player as to what music is preferred and under what conditions. After an initial training period, the XPod is able to use its internal algorithms to make an educated selection of the song that would best fit its user's emotion and situation. We use the data gathered from a streaming version of the BodyMedia SenseWear to detect different levels of user activity and emotion. After determining the state of the user the neural network engine compares the user's current state, time, and activity levels to past user song preferences matching the existing set of conditions and makes a musical selection. The XPod system was trained to play different music based on the user’s activity level. A simple pattern was used so the state dependant customization could be verified. XPod successfully learned the pattern of listening behavior exhibited by the test user. As the training proceeded the XPod learned the desired behavior and chose music to match the preferences of the test user. XPod automates the process of choosing music best suited for a user’s current activity. The success of the initial implementation of XPod concepts provides the basis for further exploration of human- and emotion-aware mobile music players.==== After Reading ====
It is a prototype application. They collect acceleration, galvanic skin response(GSR), skin temperature, heart flow, and near body temperature as input data. By using fully connected neural network, the system predict whether the user will skip this song. It looks like the product recently provide by Nike and Adidas.
But I think that the claim of song decision by emotion detecting is a little weak. Mostly they did is putting the bio-sensor signals into NN. They considered the changing of the signals. They did not explain how to consider the emotion and how they inference the result.
Moreover, I think some bio signals can help the recognition of activity, especially the `actively'.
Friday, December 22, 2006
[Reading] A Study in Users' Physiological Response to an Empathic Interface Agent
Helmut Prendinger, Christian Becker, and Mitsuru Ishizuka.
A study in users' physiological response to an empathic interface agent. [pdf]
International Journal of Humanoid Robotics, Vol. 3, No. 3, Sept. 2006, pp 371-391.
http://www.worldscinet.com/191/03/0303/S0219843606000801.html
Game: Skip-Bo
Emotion recognition: SC, EMG, & game sate(time) as input, 2-axes (arousal & valence) as output. (ref29)
Method: ANOVA
Subject: 32 (14m, 18f)
Hypothesis:
A study in users' physiological response to an empathic interface agent. [pdf]
International Journal of Humanoid Robotics, Vol. 3, No. 3, Sept. 2006, pp 371-391.
http://www.worldscinet.com/191/03/0303/S0219843606000801.html
Game: Skip-Bo
Emotion recognition: SC, EMG, & game sate(time) as input, 2-axes (arousal & valence) as output. (ref29)
Method: ANOVA
Subject: 32 (14m, 18f)
Hypothesis:
- If the virtual game opponent behaves ``naturally'' in that if follows its own goals and expresses associated positively or negatively valenced affective behaviors, users will be less aroused or stressed than when the agent does not do so.
- If the game opponent is oriented only toward its own goals and displays associated behaviors, users will be less aroused or stressed than when the agent does not express any emotion at all.
- non-emotional
- self-centered emotional
- negative empathic
- positive empathic
- auditory speech
- facial
- body gesture
- The positive empathic condition was experienced as significantly more arousaling or stressful than the negative empathic condition.
- Users seemingly do not respond significantly different when empahtic agent behavior is absent.
- EMG high, negative valence more
- global baseline -> for individual differences
a good word to use
Tuesday, December 05, 2006
[listen] Brooky's proposal
- Learn ability - features number v.s training set size (how many data should I have to train a trusty model?)
- Technical detail of methods - such as SVN, DBN
- CRF how to use.
Experiment Prework
The induction has been record as many sound files. I have make some movie clips for different mood induction. I will finish the flash for connecting database and different playing tomorrow or on Thursday.
Something need to do is to ask SY to borrow the bio-sensors. I need time....!!!
So many things need to do.
Something need to do is to ask SY to borrow the bio-sensors. I need time....!!!
So many things need to do.
Tuesday, November 28, 2006
Experiment Design
The early version has been done, but there still have something needs to be done.
- make the self-evaluation table. By degree
- 平靜＆自我評估的順序誰先誰後？還是整個做完才做自我評估
- 表情以及動作的enhancement要如何放入整個場景
- 平靜的詳細指導
- 事後處理的說明
- music choice
Friday, November 17, 2006
[NetArt] table
Some about the data we need to keep
User information:
User information:
- Id
- nickname
- password
- image
- ...
- book id
- book name
- ISBN
- cover(image)
- side(image)
- writer
- publisher
- pages
- publish time
- ranking
- ...
- who give the comment (user id)
- time
- which book (book id)
- content
- ranking
- ...
- linking id
- user id (owner)
- book id (be owned)
- date (the time the relation happened)
- 要如何表示在書架中的位置
- same book, different version
- same book, different cover
- same book, different language
- different book, same ISBN (journal?)
- ...
- 擴充性
- different between a user and a 2nd-hand book seller
- the "location of book in the book shelf"
- one people have only one bookshelf or many?
- .... (I am still thinking)
Wednesday, November 15, 2006
[Reading] From Physiological Signals to Emotion: Implementing and Comparing Selected Methods for Feature Extraction and Classification
Wagner, J. Jonghwa Kim Andre, E.
This paper appears in: Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on
Publication Date: 6-8 July 2005
On page(s): 940- 943
ISBN: 0-7803-9331-7
Digital Object Identifier: 10.1109/ICME.2005.1521579
Posted online: 2005-10-24 09:39:34.0
elicitation method: music
elicited emotion: joy, anger, sadness, pleasure (arousal & valance)
measurement: SC, EMG, ECG, RSP
analysis method: kNN, LDF(linear discriminative, function), multi-layer perceptron
result: 9x%, better then the MIT one.
This paper appears in: Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on
Publication Date: 6-8 July 2005
On page(s): 940- 943
ISBN: 0-7803-9331-7
Digital Object Identifier: 10.1109/ICME.2005.1521579
Posted online: 2005-10-24 09:39:34.0
elicitation method: music
elicited emotion: joy, anger, sadness, pleasure (arousal & valance)
measurement: SC, EMG, ECG, RSP
analysis method: kNN, LDF(linear discriminative, function), multi-layer perceptron
result: 9x%, better then the MIT one.
[Reading] Emotion Analyzing Method Using Physiological State
Mera, Kazuya and Ichimura, Takumi
1. using uttrance in dialoge to fine the emotion (by ECG)
2. using the method proposed by R. Picard to recognize the emotion (extrating 20 features)
3. using NN to combine 1 + 2
Condiser 20 emotions which are classified into 6 emotion groups :
| Book Series | Lecture Notes in Computer Science |
| Publisher | Springer Berlin / Heidelberg |
| ISSN | 0302-9743 |
| Subject | Computer Science |
| Volume | Volume 3214/2004 |
| Book | Knowledge-Based Intelligent Information and Engineering Systems |
| DOI | 10.1007/b100910 |
| Copyright | 2004 |
| ISBN | 978-3-540-23206-3 |
| Pages | 195-201 |
1. using uttrance in dialoge to fine the emotion (by ECG)
2. using the method proposed by R. Picard to recognize the emotion (extrating 20 features)
3. using NN to combine 1 + 2
Condiser 20 emotions which are classified into 6 emotion groups :
- Well-Being: joy, distress
- Fortunes-of-Others: happy-for, gloating, resentment, sorry-for
- Prospect-based: hpe, fear
- Confirmation: satisfaction, relief, fears-confirmed, disappointment
- Attribution: pride, admiration, shame, disliking
- Well-Being/Attribution: gratitude, anger, gratification, remorse
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