a board has 16 port to control the environment. Can connect LED
 clipped from www.parallax.com
Stock#: BASIC Stamp 2 Module
|
Tuesday, May 01, 2007
Control LED
The BASIC Stamp 2 is a 24-pin DIP (Dual inline package) module. Most commonly referred to as a microcontroller, on occasion you may see it being called a single board computer since it has its very own processor, memory, clock, and 
Tuesday, April 24, 2007
SVM v2 result
F_.v1.v2.Test
t c=32.0, g=0.125 CV rate=94.8725
Training...
Output model: F_v1.v2.Train.model
Scaling testing data...
Testing...
Accuracy = 46.9498% (1647/3508) (classification)
Output prediction: F_v1.v2.Test.predict
answer
H, A, S, F, P |Predict
48 69 103 27 264 |0
9 7 2 63 78 |1
0 24 46 3 7 |2
6 7 15 46 77 |3
287 187 184 449 1500 |4
=============
F0_.v1.v2.Test
t c=32.0, g=0.125 CV rate=94.7875
Training...
Output model: F0_v1.v2.Train.model
Scaling testing data...
Testing...
Accuracy = 52.1095% (1828/3508) (classification)
Output prediction: F0_v1.v2.Test.predict
answer
H, A, S, F, P |Predict
47 44 50 1 159 |0
8 2 0 46 56 |1
0 23 72 24 12 |2
1 5 12 26 18 |3
294 220 216 491 1681 |4
t c=32.0, g=0.125 CV rate=94.8725
Training...
Output model: F_v1.v2.Train.model
Scaling testing data...
Testing...
Accuracy = 46.9498% (1647/3508) (classification)
Output prediction: F_v1.v2.Test.predict
answer
H, A, S, F, P |Predict
48 69 103 27 264 |0
9 7 2 63 78 |1
0 24 46 3 7 |2
6 7 15 46 77 |3
287 187 184 449 1500 |4
=============
F0_.v1.v2.Test
t c=32.0, g=0.125 CV rate=94.7875
Training...
Output model: F0_v1.v2.Train.model
Scaling testing data...
Testing...
Accuracy = 52.1095% (1828/3508) (classification)
Output prediction: F0_v1.v2.Test.predict
answer
H, A, S, F, P |Predict
47 44 50 1 159 |0
8 2 0 46 56 |1
0 23 72 24 12 |2
1 5 12 26 18 |3
294 220 216 491 1681 |4
J48 v1 result
F_v1.csv(10 CV)
=== Run information ===
Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: F_v1
Instances: 6274
Attributes: 103
[list of attributes omitted]
Test mode: 10-fold cross-validation
=== Classifier model (full training set) ===
=== Summary ===
Correctly Classified Instances 5823 92.8116 %
Incorrectly Classified Instances 451 7.1884 %
Kappa statistic 0.8832
K&B Relative Info Score 552448.6144 %
K&B Information Score 9940.3112 bits 1.5844 bits/instance
Class complexity | order 0 11285.6028 bits 1.7988 bits/instance
Class complexity | scheme 327256.4897 bits 52.1607 bits/instance
Complexity improvement (Sf) -315970.8869 bits -50.362 bits/instance
Mean absolute error 0.0316
Root mean squared error 0.1657
Relative absolute error 12.8354 %
Root relative squared error 47.2221 %
Total Number of Instances 6274
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure Class
0.923 0.006 0.942 0.923 0.933 _S
0.845 0.012 0.851 0.845 0.848 _A
0.941 0.013 0.925 0.941 0.933 _F
0.861 0.017 0.856 0.861 0.858 _H
0.948 0.07 0.949 0.948 0.949 _P
=== Confusion Matrix ===
a b c d e <-- classified as
553 4 14 1 27 | a = _S
8 382 7 8 47 | b = _A
1 15 859 5 33 | c = _F
1 6 8 570 77 | d = _H
24 42 41 82 3459 | e = _P
Number of Leaves : 278
Size of the tree : 555
================
F0_v1.csv (10 CV)
=== Run information ===
Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: F0_v1
Instances: 6274
Attributes: 103
[list of attributes omitted]
Test mode: 10-fold cross-validation
=== Classifier model (full training set) ===
Number of Leaves : 262
Size of the tree : 523
Time taken to build model: 34.48 seconds
=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances 5742 91.5206 %
Incorrectly Classified Instances 532 8.4794 %
Kappa statistic 0.8615
Mean absolute error 0.0364
Root mean squared error 0.1782
Relative absolute error 14.7968 %
Root relative squared error 50.7918 %
Total Number of Instances 6274
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure Class
0.93 0.008 0.922 0.93 0.926 _S
0.812 0.012 0.838 0.812 0.825 _A
0.92 0.015 0.911 0.92 0.916 _F
0.802 0.016 0.854 0.802 0.827 _H
0.945 0.092 0.935 0.945 0.94 _P
=== Confusion Matrix ===
a b c d e <-- classified as
557 7 10 1 24 | a = _S
9 367 16 8 52 | b = _A
5 12 840 5 51 | c = _F
1 8 8 531 114 | d = _H
32 44 48 77 3447 | e = _P
=== Run information ===
Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: F_v1
Instances: 6274
Attributes: 103
[list of attributes omitted]
Test mode: 10-fold cross-validation
=== Classifier model (full training set) ===
=== Summary ===
Correctly Classified Instances 5823 92.8116 %
Incorrectly Classified Instances 451 7.1884 %
Kappa statistic 0.8832
K&B Relative Info Score 552448.6144 %
K&B Information Score 9940.3112 bits 1.5844 bits/instance
Class complexity | order 0 11285.6028 bits 1.7988 bits/instance
Class complexity | scheme 327256.4897 bits 52.1607 bits/instance
Complexity improvement (Sf) -315970.8869 bits -50.362 bits/instance
Mean absolute error 0.0316
Root mean squared error 0.1657
Relative absolute error 12.8354 %
Root relative squared error 47.2221 %
Total Number of Instances 6274
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure Class
0.923 0.006 0.942 0.923 0.933 _S
0.845 0.012 0.851 0.845 0.848 _A
0.941 0.013 0.925 0.941 0.933 _F
0.861 0.017 0.856 0.861 0.858 _H
0.948 0.07 0.949 0.948 0.949 _P
=== Confusion Matrix ===
a b c d e <-- classified as
553 4 14 1 27 | a = _S
8 382 7 8 47 | b = _A
1 15 859 5 33 | c = _F
1 6 8 570 77 | d = _H
24 42 41 82 3459 | e = _P
Number of Leaves : 278
Size of the tree : 555
================
F0_v1.csv (10 CV)
=== Run information ===
Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: F0_v1
Instances: 6274
Attributes: 103
[list of attributes omitted]
Test mode: 10-fold cross-validation
=== Classifier model (full training set) ===
Number of Leaves : 262
Size of the tree : 523
Time taken to build model: 34.48 seconds
=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances 5742 91.5206 %
Incorrectly Classified Instances 532 8.4794 %
Kappa statistic 0.8615
Mean absolute error 0.0364
Root mean squared error 0.1782
Relative absolute error 14.7968 %
Root relative squared error 50.7918 %
Total Number of Instances 6274
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure Class
0.93 0.008 0.922 0.93 0.926 _S
0.812 0.012 0.838 0.812 0.825 _A
0.92 0.015 0.911 0.92 0.916 _F
0.802 0.016 0.854 0.802 0.827 _H
0.945 0.092 0.935 0.945 0.94 _P
=== Confusion Matrix ===
a b c d e <-- classified as
557 7 10 1 24 | a = _S
9 367 16 8 52 | b = _A
5 12 840 5 51 | c = _F
1 8 8 531 114 | d = _H
32 44 48 77 3447 | e = _P
SVM v1 result
F_v1.test.5
Best c=128.0, g=0.5 CV rate=93.2173
Training...
Output model: F_v1.train.5.model
Scaling testing data...
Testing...
Accuracy = 94.8791% (1334/1406) (classification)
Output prediction: F_v1.test.5.predict
answer
H, A, S, F, P |Predict
129 0 0 1 13 |0
0 88 1 0 2 |1
1 0 124 0 2 |2
0 1 2 188 3 |3
20 11 3 12 805 |4
=========
F0_v1.test.5
Best c=32.0, g=0.5 CV rate=93.0753
Training...
Output model: F0_v1.train.5.model
Scaling testing data...
Testing...
Accuracy = 94.8791% (1334/1406) (classification)
Output prediction: F0_v1.test.5.predict
answer
H, A, S, F, P |Predict
129 0 0 1 12 |0
0 86 1 0 3 |1
1 0 124 0 2 |2
0 0 2 189 2 |3
20 14 3 11 806 |4
Best c=128.0, g=0.5 CV rate=93.2173
Training...
Output model: F_v1.train.5.model
Scaling testing data...
Testing...
Accuracy = 94.8791% (1334/1406) (classification)
Output prediction: F_v1.test.5.predict
answer
H, A, S, F, P |Predict
129 0 0 1 13 |0
0 88 1 0 2 |1
1 0 124 0 2 |2
0 1 2 188 3 |3
20 11 3 12 805 |4
=========
F0_v1.test.5
Best c=32.0, g=0.5 CV rate=93.0753
Training...
Output model: F0_v1.train.5.model
Scaling testing data...
Testing...
Accuracy = 94.8791% (1334/1406) (classification)
Output prediction: F0_v1.test.5.predict
answer
H, A, S, F, P |Predict
129 0 0 1 12 |0
0 86 1 0 3 |1
1 0 124 0 2 |2
0 0 2 189 2 |3
20 14 3 11 806 |4
Wednesday, April 11, 2007
BVP analysis
analysis of BVP
clipped from www.micromedical.co.ukPulse Contour Analysis
Cardiovascular disease (CVD) is the leading cause of death and serious illness and in 1948, the Framingham Heart Study embarked on an ambitious project in health research. Pulse wave shape was one of the parameters collected during the study. The tools available to the investigators at that time precluded a detailed analysis of the waveform, but visual inspection of waveform changes correlated with increased risk of developing CVD (Ref.1 & 20). It is only recently that research workers from around the world have revisited this exciting observation (Ref. 2 to 5, 28, 29, 31) and in particular the research group at St Thomas hospital showed that the finger volume pulse derived from a digital photoplethysmographic probe is directly related to the radial and brachial artery pressure pulse (Ref. 6).
The Digital Volume Pulse (DVP)
The digital volume pulse (DVP) is recorded by measuring the transmission of infra-red light absorbed through the finger. The amount of light is directly proportional to the volume of blood in the finger pulp.
To minimise the occurrence of poor signals from vasoconstricted and poorly perfused subjects, a unique control system maintains the light transmission at the optimum level to accurately follow blood volume changes, independant of the subjects finger size to obtain an extremely accurate and noise free signal.
How the Digital Volume Pulse (DVP) is formed?
The first part of the waveform (systolic component) is formed as a result of pressure transmission along a direct path from the aortic root to the finger. The second part (diastolic component) is formed by pressure transmitted from the ventricle along the aorta to the lower body where it is reflected back along the aorta to the finger. The upper limb provides a common channel for both the directly transmitted pressure wave and the reflected wave and, therefore, has little influence on the contour of the DVP.
Indices derived from the Digital Volume Pulse (DVP)
The height of the diastolic component of the DVP relates to the amount of pressure wave reflection. This in turn relates mainly to the tone of small arteries.
The timing of the diastolic component relative to the systolic component depends on the pulse wave velocity (PWV) of pressure waves in the aorta and large arteries. This in turn depends upon large artery stiffness.
Indices derived from the Digital Volume Pulse (DVP)
Reflection Index RI is the height of the diastolic component of the DVP expressed as a percentage of the systolic peak and is a measure of the amount of pulse wave reflection and the tone of small arteries:
The Stiffness Index SI is an estimate of pulse wave velocity in large arteries and is obtained from subject height divided by the time between the systolic and diastolic peaks of the DVP. It is a measure of large artery stiffness
To see more clips or start creating your own, visit clipmarks.com
Tuesday, April 10, 2007
FFT
Fs是指sample rate
y是要被做FFT的data, 長度為L
NFFT = 2^nextpow2(L); % Next power of 2 from length of y
Y是做完FFT的結果
y是要被做FFT的data, 長度為L
NFFT = 2^nextpow2(L); % Next power of 2 from length of y
Y是做完FFT的結果
f是在frequence domain時的橫軸座標, 因此橫軸座標是的間隔是1/NFFT*2*Fs/2= 1*Fs/NFFT
|
補上一筆
只有一半可以用
clipped from civil.njtc.edu.tw假如原來在時間域的資料點有N點，則經過FFT轉換後，在頻率域其數據仍為N點，但己經是複數了。這N點中第一個點為所有其他點的總合，而且前半數的N/2點與後半數的N/2點是共軛對稱的複數，對稱點在中點，例如有256點，則128點為其共軛對稱點。因此在時間域有N點，則以FFT轉換到頻率頻後只有N/2點可用。至於各點頻率差，若在時間域的數據每點間隔
則在頻率域的頻率間隔為
，例如前述的A900地震儀，其
=0.005，取N=1024點做FFT轉換後可用點只有其半數512點，各點頻率間隔為
=0.1953125cps.
 |  |
Power Spectrum Density
x: data
window: 在data中考量的window的大小
noverlap: window和window之間overlap的sample個數
nfft: 做fft時window的大小
window: 在data中考量的window的大小
noverlap: window和window之間overlap的sample個數
nfft: 做fft時window的大小
clipped from www.mathworks.com
|
Monday, April 09, 2007
一直忘了補上average
再取average的時候
到底怎樣取才好勒....
我現在有兩種取法,
一種是在整個induction的過程中都拿來average.
一種是將切出來的各種檔案拿來average
好像第二種比較make sense. ! so?
到底怎樣取才好勒....
我現在有兩種取法,
一種是在整個induction的過程中都拿來average.
一種是將切出來的各種檔案拿來average
好像第二種比較make sense. ! so?
smooth?! normalized?!
smooth and normalized是最近在想的問題.
該怎麼寫比較準確, 該怎麼弄比較好
都是要考慮的....
smooth現在寫了兩個方法, gaussian smooth filter(用guassian window去做convorution)
另一個是標準的law pass filter(span=5).
其他其實還有很多的方法, 但感覺不出好壞與優劣還有特點
感覺上我應該要依據訊號的特徵去處理
現在是傾向寫起來放著 XD
除此之外, normalized其實就是shift and scale.
再放入feature的時候真的有這個必要嗎?!
真是不知道哪個好....
一樣先寫起來放著吧.... XD
快寫完了, 我要快點train
該怎麼寫比較準確, 該怎麼弄比較好
都是要考慮的....
smooth現在寫了兩個方法, gaussian smooth filter(用guassian window去做convorution)
另一個是標準的law pass filter(span=5).
其他其實還有很多的方法, 但感覺不出好壞與優劣還有特點
感覺上我應該要依據訊號的特徵去處理
現在是傾向寫起來放著 XD
除此之外, normalized其實就是shift and scale.
再放入feature的時候真的有這個必要嗎?!
真是不知道哪個好....
一樣先寫起來放著吧.... XD
快寫完了, 我要快點train
Sunday, March 25, 2007
Progress!
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%
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%
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.
Subscribe to:
Posts (Atom)
