車輛駕駛外文翻譯--智能車的情緒與情感識(shí)別接口（英文）

1、 1Affective Intelligent Car Interfaces with Emotion Recognition Christine L. Lisetti Department of Multimedia Communications, Institut Eurecom Sophia-Antipolis, France christine.lisetti@eurecom.fr Fatma Nasoz Schoo

2、l of Computer Science, University of Central Florida Orlando, FL fatma@cs.ucf.edu Abstract In this paper, we uncover a new potential application for multi-media technologies: affective intelligent car interfaces for

3、 enhanced driving safety. We also describe the experiment we conducted in order to map certain physiological signals (galvanic skin response, heart beat, and temperature) to certain driving-related emotions and states

4、(Frustration/Anger, Panic/Fear, and Boredom/Sleepiness). We demonstrate the results we obtained and describe how we use these results to facilitate a more natural Human-Computer Interaction in our Multimodal Affective

5、Car Interface for the drivers of the future cars. 1 Introduction and Motivation Humans are social beings that emote and their cognition is affected by their emotions. Emotions influence various cognitive processes in h

6、umans, including perception and organization of memory (Bower, 1981), categorization and preference (Zajonc, 1984), goal generation, evaluation, and decision-making (Damasio, 1994), strategic planning (Ledoux, 1992), f

7、ocus and attention (Derryberry Ekman Chovil 1991), and learning (Goleman, 1995). Previous studies also suggest that people emote while they are interacting with computers (Reeves Gross & Levenson, 1997). Howe

8、ver, interpreting the data with statistical methods and algorithms is beneficial in terms of actually being able to map them to specific emotions. Studies have demonstrated that algorithms can be very successfully impl

9、emented for recognition of emotions from physiological signals. Collet et al. (Collet, Vernet-Maury, Delhomme, & Dittmar, 1997) showed neutral and emotionally loaded pictures to participants in order to elicit happ

10、iness, surprise, anger, fear, sadness, and disgust. The physiological signals measured were: Skin conductance (SC), skin potential (SP), skin resistance (SR), skin blood flow (SBF), skin temperature (ST), and Instantan

11、eous respiratory frequency (IRF). Statistical comparison of data signals was performed pair-wise, where 6 emotions formed 15 pairs. Out of these 15 emotion-pairs, electrodermal responses (SR, SC, and SP) distinguished

12、13 pairs, and similarly combination of thermo-circulatory variables (SBF and ST) and Respiration could distinguish 14 emotion pairs successfully. Picard et al. (Picard, Healey, & Vyzas, 2001) used personalized imag

13、ery and emotionally loaded pictures to elicit happiness, sadness, anger, fear, disgust, surprise, neutrality, platonic love, and romantic love. The physiological signals measured were GSR, heartbeat, respiration, and e

14、lectrocardiogram. The algorithms used to analyze the data were Sequential Forward Floating Selection (SFFS), Fisher Projection, and a hybrid of these two. The best classification achievement was gained by the hybrid

15、 method, which resulted in 81% overall accuracy. Healey’s research (Healey, 2000) was focused on recognizing stress levels of drivers by measuring and analyzing their physiological signals (skin conductance, heart activ

16、ity, respiration, and muscle activity). During the experiment participants of this study drove in a parking garage, in a city, and on a highway. Results showed that the drivers’ stress could be recognized as being rest

17、 (i.e. resting in the parking garage), city (i.e. driving in the Boston streets), and highway (i.e. two lane merge on the highway) with 96% accuracy. 2.2 Our Preliminary Emotion Elicitation and Recognition Experiments

18、 In our emotion elicitation experiment we used movie clips and difficult mathematical questions to elicit six emotions: sadness, anger, surprise, fear, frustration, and amusement and a non-invasive wireless wearable com

19、puter – BodyMedia SenseWear Armband (Figure 2) – to collect the physiological signals of our participants: galvanic skin response, heart rate, and temperature. Figure 2 BodyMedia SenseWear Armband Mathematical question