[Reading] XPOD - A Human Activity and Emotion Awake Mobile Music Player

http://ebiquity.umbc.edu/paper/html/id/280/

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'.

[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:

• 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.
  

Agent's behavior:

• non-emotional
• self-centered emotional
• negative empathic
• positive empathic
  

Agent's abilities: (in PAD space)

• auditory speech
• facial
• body gesture

Result:

• 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.
  

Notes:

• EMG high, negative valence more
• global baseline -> for individual differences
  

a good word to use

  empathy
      n : understanding and entering into another's feelings

This is what I want the computer to know and to do.

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
  

This is tomorrow's list..

[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.

[Reading] Emotion Analyzing Method Using Physiological State

Mera, Kazuya and Ichimura, Takumi

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
  

A method combine context and physiological signal processing to detect the clients emotion.

[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)

[readings] Multimodal Emotion Recognition & Expressivit Analysis

Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on

Kollias, S. Karpouzis, K.


The paper present the framwork of multimodal emotion recognition and expressivity analysis. First, it introduces technologis that involve in emotion reocognition, such as speech, visaul, physiological signal processing, etc. Then, it mentions the important issues in emotional speech analysis, visual analysis, ECA expressivity, and physiological signal analysis in sections. Finally, some key problem identified for integration of sigle-mode emotion analysis techniques are listed.
I think this is a high level paper which shows up and lists the problem in the field of emotion recognition. The cited papers are distribute in three fields, which are speech, facial expression, and physiological signal. I will read more and part 3, the physiological signal process with emotion recognition.

[∞]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.

[Readings]Evolving Pet Robot with Emotional Model

Kubota, N. Nojima, Y. Baba, N. Kojima, F. Fukuda, T.

This paper appears in: Evolutionary Computation, 2000. Proceedings of the 2000 Congress on
Publication Date: 2000
Volume: 2, On page(s): 1231-1237 vol.2
ISBN: 0-7803-6375-2
References Cited: 23
Digital Object Identifier: 10.1109/CEC.2000.870791

In daily, many pet robots have been existed. Writers show a pet robot with emotion model. They discribe the details in the paper. First, they have some sensors in the robot. People can interact with the robot via the sensors. The sensors' signal inputs feed into emotional model which is desing by themself. (The model include emotion, mood, and feelings.) Robots are express their emotion by actions such as moving, dancing, and sounding. Moreover, they use potential field to represent a trick. They try to teach the robot the ``trick''. This is the step of behavioral learning. Finally, they use GA to optimal fuzzy controllers and path planning for mobile robots.

In this paper, everything the robot feels is defined by human. I think the writers model the robot's behavior with emotional concept. But all the thing about the model is predefine without animal phsychology. It really work, but it is pre-define. That is another side of emotional using. I am not focusing this part now.

[Readings] Aritficail Emotion and Its Recognition, Modeling and Applications: An Overview

This paper really an overview. The writers using many reference to discribe the whole picture of artificial emotion. They using the view of medical science. But in the book I recently read, it is just one part of theory in the whole emotion psychology. Moreover, they discribe differenct emotion recognition methods. In the emotion modeling, analytic and symbolic method and botom-up method are both mentioned.

I think this is really an overview paper and no technical details inside. What I should do is to read the reference and to find the recognition method.

By the way, it is really strange that the papar is writen in simplify Chinese and published in Proceeding of the 5th World Cngress on Intelligent Control and Automatin. The conference was held in China. I think this is the reason why it is writen in Chinese.

emotion design

The book ``Emotion Design'' tell us why we love an artifac in the view of psycoology. The writer of the book disscuss the daily products, games, computers, and robotics. He using the model with three levels to describe the human actions and reflect the design. The three levels are visceral(本能), behaviofal, and reflective. Many issues discribing the interaction between the computer and human.

I get some idea from the book. I am trying to add the emotion to a computer system, but I still confuse about how to add into it and how much I should add. I think I should add core effect into the system. (System dependency?) I don't need to add too may emotions to the system. I just need to add those which are correct and useful. In the future, I should do some experiment about the `effect' of emotions.

I don't want to build an emotion model myself, I will try to use the exsit one. Now I need to do is choosing a good application to prove the emotion is important in a computer system.

5 model of emotion

I read the first part of the book ``What is an Emotion''. It mentions five differnt view and research area in the emotion due to the personal phylosophy. They are

• Considering emotions and metal phenomena are different. (distinguish their differnce)
  
• Grouping emtions into generic type. (classification)
  
• Using the physiological changes to detect emotional changes. (Dominant now, using the physiology)
  
• Role of emotion. (Social effect of emotion, person to person effect, etc. )
  
• Analyzing emotion into its components or aspects.
  

Because of the differnt phylosophys, there are diffent theories, such as sensation theories, physiological theories, behavioral theories, evaluative theories, and cognitive theories.

group meeting (more about ColorCocktail)

1. Hue 在Color當中要怎麼把它放進去?
2. 選擇畫畫, 分析情緒, 產生文字, 對應show給input user看, 作cocktail recommandation?
3. Error的算法 (快點看懂)
4. 相關的paper of color on emotions.
5. 大家喝cocktail會在乎甜味, 酒味
6. Draw without context?