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From Motion to Emotion: Accelerometer Data Predict Subjective Experience of Music
Melanie Irrgang1, Hauke Egermann1,2
1Audio Communication Group, Technische Universität Berlin, Berlin, Germany.
Plos One
|July 15, 2016
Summary
Mobile device acceleration data from free movement during music listening can predict emotional responses. This research links music-evoked movement to feelings, aiding embodied music cognition and recommender systems.
Area of Science:
- Cognitive Science
- Music Psychology
- Human-Computer Interaction
Background:
- Music is widely recognized for its emotional impact, often linked to expressive movements.
- Assessing music-evoked movement offers a potential method for measuring musical emotions.
Purpose of the Study:
- To evaluate the predictive power of mobile device acceleration data from free movement during music listening for musical emotion ratings.
- To explore the relationship between music-evoked movement patterns and experienced feelings.
Main Methods:
- Two experiments were conducted using mobile-device generated acceleration data.
- Participants' free movement during music listening was recorded.
- Data were used to predict ratings on the Geneva Emotion Music Scales (GEMS-9).
Main Results:
- Acceleration data showed varying predictive power for different GEMS dimensions, with notable success for tenderness, nostalgia, power, and tension.
- Both arousal and valence, key components of emotional experience, were predictable from acceleration data.
- Findings indicate that movement patterns contain information about the quantity and quality of musical emotions.
Conclusions:
- Music-evoked movement patterns are demonstrably related to music-evoked feelings.
- This research supports the integration of embodied music cognition principles into music recommender systems.
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