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
Friday, January 26, 2007
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)
