Perceiving affect from arm movement

Pollick, Frank E.; Paterson, Helena; Bruderlin, Armin; Sanford, Anthony J.

doi:10.1016/s0010-0277(01)00147-0

Cited by 542 publications

(374 citation statements)

References 25 publications

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“…However, by taking into account individual idiosyncrasies in the description of the movement, the performances increased to 81%. The recognition performances were comparable to human observers' performances (varying between 59% and 71%) for the same set of stimuli, as discussed in [2]. Another interesting study aimed at recognizing affective states is the one by Gunes and Piccardi [20].…”

Section: Introductionsupporting

confidence: 66%

“…Until recently, the main modality used to measure the affective state of people was their facial expressions [4]. Recent psychology studies, however, have revealed that body expressions are also a very good indicator of affect [e.g., 2,3,10]. These studies encouraged us into researching the possibility to create an automatic recognition system that would use the players' body movement to detect their affective state.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Recognition of Affective Body Movement in a Video Game Scenario

Savva

Bianchi-Berthouze

2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. This study aims at recognizing the affective states of players from non-acted, non-repeated body movements in the context of a video game scenario. A motion capture system was used to collect the movements of the participants while playing a Nintendo Wii tennis game. Then, a combination of body movement features along with a machine learning technique was used in order to automatically recognize emotional states from body movements. Our system was then tested for its ability to generalize to new participants and to new body motion data using a sub-sampling validation technique. To train and evaluate our system, online evaluation surveys were created using the body movements collected from the motion capture system and human observers were recruited to classify them into affective categories. The results showed that observer agreement levels are above chance level and the automatic recognition system achieved recognition rates comparable to the observers' benchmark.

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Section: Introductionsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Affective Body Movement in a Video Game Scenario

Savva

Bianchi-Berthouze

2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Point-light displays, initially developed by Johansson (1973), therefore constitute a popular form of impoverished visual stimuli to investigate the contribution of motion (kinematic and form-from-motion) cues to observers' ratings of personality and other trait impressions. Previously, it has been shown that observers reliably judge transient states such as emotions from point-light displays showing movements of the whole-body (Atkinson, Dittrich, Gemmell, & Young, 2004;Dittrich, Troscianko, Lea, & Morgan, 1996;Heberlein, et al, 2004) or of the arm alone (Pollick, Paterson, Bruderlin, & Sanford, 2001). They also reliably judge stable characteristics such as identity Loula, Prasad, Harber, & Shiffrar, 2005) and sex from point-light whole-body motion.…”

Section: Introductionmentioning

confidence: 99%

“…Although kinematic analyses of gait and other whole-body movement have been used to discover which visual cues drive perception of sex Mather & Murdoch, 1994;Troje, 2002), vulnerability (Gunns, Johnston, & Hudson, 2002;Johnston, Hudson, Richardson, Gunns, & Garner, 2004) or emotion (Pollick, et al, 2001;Roether, Omlor, Christensen, & Giese, 2009), we here present for the first time a kinematic analysis of personality trait judgments.…”

Section: Introductionmentioning

confidence: 99%

First impressions: Gait cues drive reliable trait judgements

2012

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Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in Cognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be re ected in this document. Changes may have been made to this work since it was submitted for publication. A de nitive version was subsequently published in Cognition, 124(3), September 2012, 10.1016/j.cognition.2012.05.018Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.

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“…In fact, neurophysiological investigations of nonhuman primates as well as brain imaging work in humans have provided first hints that the brain is more responsive to conspecific action and movement patterns than it is to static images of a human figure. [18][19][20][21] The movement of a human body contains information about identity 22 as well as a variety of different attributes such as sex, [23][24][25] emotional states, 26,27 and body weight. [28][29][30] All of these attributes are socially relevant 31 and probably play an important role in body image formation.…”

Section: Introductionmentioning

confidence: 99%

Static and dynamic body image in bulimia nervosa: Mental representation of body dimensions and biological motion patterns

Vocks

Legenbauer

Rüddel

et al. 2006

Intl J Eating Disorders

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Objective: The aim of the present study was to find out whether in bulimia nervosa the perceptual component of a disturbed body image is restricted to the overestimation of one's own body dimensions (static body image) or can be extended to a misperception of one's own motion patterns (dynamic body image).Method: Participants with bulimia nervosa (n ¼ 30) and normal controls (n ¼ 55) estimated their body dimensions by means of a photo distortion technique and their walking patterns using a biological motion distortion device.Results: Not only did participants with bulimia nervosa overestimate their own body dimensions, but also they perceived their own motion patterns corresponding to a higher BMI than did controls. Static body image was correlated with shape/ weight concerns and drive for thinness, whereas dynamic body image was associated with social insecurity and body image avoidance. Conclusion:In bulimia nervosa, body image disturbances can be extended to a dynamic component.

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Perceiving affect from arm movement

Cited by 542 publications

References 25 publications

Automatic Recognition of Affective Body Movement in a Video Game Scenario

Automatic Recognition of Affective Body Movement in a Video Game Scenario

First impressions: Gait cues drive reliable trait judgements

Static and dynamic body image in bulimia nervosa: Mental representation of body dimensions and biological motion patterns

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