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

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

clipped from www.mathworks.com Y = fft(y,NFFT)/L; f = Fs/2*linspace(0,1,NFFT/2);

http://civil.njtc.edu.tw/weng/excel/lectureNote/2.8.htm
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.

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?

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

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%

[Writing] Tech writing

https://ceiba.ntu.edu.tw/modules/bulletin/bulletin.php?current_lang=english&csn=a96624#

Tech writing

[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!!

論文開工

最近忙私事
忙到壓力很大,
但是論文總是開工了!
加油吧...

專心做一件事情的感覺
舒服的多
比壓力小的多

[Readings] Emotion recognition form physiological signals using wireless sensors for presence techonologies

Journal	Cognition, Technology & Work
Publisher	Springer London
ISSN	1435-5558 (Print) 1435-5566 (Online)
Subject	Computer Science and Engineering
Issue	Volume 6, Number 1 / February, 2004
Category	Original Article
DOI	10.1007/s10111-003-0143-x
Pages	4-14
Online Date	Thursday, February 19, 2004

Fatma Nasoz¹ , Kaye Alvarez², Christine L. Lisetti¹ and Neal Finkelstein

subject: 29
elicitation method: movie
method: kNN(71%), DFA(74%), MBP(83%)
elicited emotioin: sadness anger surprise fear frustration amusement (by 1995 Gross Levenson)
Measure: GSR, HR, tempreture
Data saving: 3-dimensional array, real number (see in page 12)

[∞]emotion in the proposal

The part of emotion is the one people like to criticize. Definition of emotion is subjective. People can define emotion by themselves. For example, in David's talk today, he wants to find out emotions in the lyrics. The emotion in the lyrics depends on listeners emotion or writer's emotion, which we should define.
So, in my proposal, I should show up my definition of emotion. I think the emotion is the patient's emotion. Observer is helper. He/she is not the one define the emotion in my experiment.
The define above may help me.

==
Another interesting point it difference betweeen listening a sentence and reading a sentence.

[group meeting]Something reminder

about proposal

1. Problem definition need to include input, output, requirment, and assumption.
2. We need a step by step plan with mileston and checkpoints instead of feature work. The plan inclueds time.
3. What is the important of the problem should be described.
4. If everything I was done, what is my contribution?
5. Evaluation needs to be defined.
   

[ProComp] Acquiring Live Data

The following steps outline a minimal set of functions required to acquire live data from a connected encoder.

To acquire live data:
1. Initialize the COM environment by calling CoInitialize or CoInitializeEx.

• HRESULT hr = CoInitializeEx(NULL, COINIT_ APARTMENTHEREADED);
• CheckHRESULT(hr);
• SUCCEEDED(hr);
  

2. Create an instance of a TTLLiveCtrl object and query for its ITTLLive or ITTLLive2 interface.

• HRESULT hr = g_TTLLive.CreateInstance(CLSID_TTLLive);
• CheckHRESULT(hr);
• SUCCEEDED(hr);

3. Detect and open encoder connection(s) using the OpenConnection or OpenConnections method.

• g_TTLLive->OpenConnections(TTLAPI_OCCMD_AUTODETECT, 1000, NULL, NULL);
  

4. Close any unwanted connections using CloseConnection.

• g_TTLLive->CloseConnections();
  

5. Optionally define the encoder enumeration order by calling AssignEncoderHND.

• not sure yet
  

6. Create logical channels corresponding to all encoder physical channels using AutoSetupChannels.
7. Synchronously start all channels using StartChannels.
8. Retrieve data periodically using ReadChannelData or ReadChannelDataVT.
9. When finished, close all connections using CloseConnections.
10. Release queried ITTLLive or ITTLLive2 interface.
11. Match the previous call to CoInitialize or CoInitializeEx with a call to CoUninitialize

==
basic from the dacument of ProComp.

[Proposal] note

content:

• motivation
• problem definition
• survey of elated work
• proposed solution
• testing evaluation (option)

[Proposal] Coming soon..

Two weeks later, I need to propose my thesis. The main idea have been draw out, and I have a good story to tell everyone why I do this. What should I do now is working hard and writing down details.

[∞]Recently

• Resently Progress.
  
• Since the crack of my plog, I have moved my research blog to http://searchsheila.blogspot.com. The reading reports and ideas still shown in the page.
• Recently, I have reading some application and models about the emotion, I am finding a good application to do my thesis and demo the idea.
  
• In group meeting this week, others gave me some different ideas, such as making a ghost house. The ghost house is "personalized". Different peoples have different feelings of same ghost house. We can control the scene in the house to customize the ghost house. Other ideas are like ``sleeping helper'', ``living room arua controller'', etc. Here are the both way of emotion: one is possitive, the other is negative.
  
• research goal this semanster
• At the end of the semanster, I want to begin the experiment.
  
• Improve my English writing skill.
  
• resently research goal and readings
  
• emotion model choosing
• read research in MIT Media Lab Affective Computing Group.
  

Self-Cam: Feedback from wat would be your social partner

Teeters, Alea and Kaliouby, Rana El and Picard, Rosalind
(MIT Media Lab, Affective computin)

Self-cam can record 24 feature points of personal face and infer the wearer's hidden affective-cognitive states. It can identify 20 facial and head movements and 11 gestures. It uses Bayesian Network to infer the affective-cognitive states. The states includes agreeing, disagree, interested, confused, concentrating and thinking. Wearer can explore his/her apperence in the interaction.

This is a little equipment that can be used to record the feature point. I may used it to detect some personal activities and emotions. As the result, I can provide feed back to help people to do some interaction. (CHI?)

I think it looks like that poeple like to take photos themself. In many camara, the producer provides the fucntion of "self-photoing". It take advantage human's instinct. I think this may be workable in the recognition of emotions.

Ideas about app

• Music can affect the emotion
• Pictures (photo) can, too
• An emotion treatment?
  
• An enviroment make you comfortable
• The ``role'' of emotion in the app -> should approve the system