Classification of Emotions Indicated by Walking Using Motion Capture

Ishida, Yasumasa; Tanaka, Hisaya

doi:10.1007/978-3-030-50726-8_45

Cited by 1 publication

(2 citation statements)

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“…Across their five studies, they concluded there are differences in people's head movements based on their emotional state elicited by emotional cues. Ishida and Tanaka (2020) found that the emotion seen in participants' gait was predicted with 36% accuracy after an induction listening to music. Ishida and Tanaka (2021) followed this up by measuring the gait characteristics of participants displaying various emotions.…”

Section: Emotion and Motionmentioning

confidence: 97%

“…Across previous research there is evidence that movement and emotion are related; a person's movements change based on how they are feeling. However, some studies' induction into emotional states has questionable ecological validity (i.e., Behnke et al, 2021;Ishida & Tanaka, 2020, 2021. This induction, or manipulation of the current context, is important for recognising emotions well (Abramson et al, 2021) and must be performed well.…”

Section: Emotion and Motionmentioning

confidence: 99%

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Theory Based Modelling of the Relationship Between Emotional States and Bodily Activity Using Machine Learning

Goldsack

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<p>The current study uses machine learning with a theoretically derived approach to investigate the relationship between emotion and bodily activity. Previous literature focuses on the accuracy of machine learning models when predicting emotional states. The studies lack theoretical grounding in emotion literature justifying the appropriateness of the chosen machine learning model architectures in predicting emotional states. The current studies’ machine learning models use data from dyadic interactions in positively and negatively valenced conversations. The series of machine learning models apply the same architecture to predict a person’s emotional state using bodily activity. To achieve this the models are trained on multiple measures of a person’s emotional state and bodily activity across differently valenced conversations. For example, a model is trained/tested on emotion measure #1, bodily activity measure #1 and positive conversations. In contrast, another model is trained and tested on emotion measure #1, bodily activity measure #2, and negative conversations. The Test Error resulting from the machine learning models was then statistically tested to answer various questions. The machine learning models trained/tested on emotion measures including the arousal and valence of an emotional state performed the better than those including an emotion measure of how arousal changes over time. The models found that a person’s physiological activity most accurately predicts their emotional state, compared to predicting with the person’s movements. The models trained/tested on negatively valenced conversations predicted people’s emotional states better than those on positively valenced conversations. The results show that not all emotion measures are equal, and the valence and arousal of an emotional state most accurately represent the emotional state. The results replicated the close relationship between emotion and physiological activity. Finally, the context valence alters the accuracy of predicting a person’s emotional state – knowing someone’s situation provides information about their emotional state.</p>

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Section: Emotion and Motionmentioning

confidence: 97%

Section: Emotion and Motionmentioning

confidence: 99%

Theory Based Modelling of the Relationship Between Emotional States and Bodily Activity Using Machine Learning

Goldsack

View full text Add to dashboard Cite

show abstract

Classification of Emotions Indicated by Walking Using Motion Capture

Cited by 1 publication

References 1 publication

Theory Based Modelling of the Relationship Between Emotional States and Bodily Activity Using Machine Learning

Theory Based Modelling of the Relationship Between Emotional States and Bodily Activity Using Machine Learning

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