Rus, Silvia ; Joshi, Dhanashree ; Braun, Andreas ; Kuijper, Arjan (2018)
The Emotive Couch - Learning Emotions by Capacitively Sensed.
In: Procedia Computer Science, 130
doi: 10.1016/j.procs.2018.04.038
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Affective computing allows machines to simulate and detect emotional states. The most common method is the observation of the face by camera. However, in our increasingly observed society, more privacy-aware methods are worth exploring that do not require facial images, but instead look at other physiological indicators of emotion. In this work we present the Emotive Couch, a sensor-augmented piece of smart furniture that detects proximity and motion of the human body. We present the design rationale and use standard machine learning techniques to detect the three basic emotions Anxiety, Interest, and Relaxation. We evaluate the performance of our approach with 15 participants in a study that includes various affect elicitation methods, achieving an accuracy of 77.7 %.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Rus, Silvia ; Joshi, Dhanashree ; Braun, Andreas ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | The Emotive Couch - Learning Emotions by Capacitively Sensed |
| Sprache: | Englisch |
| Publikationsjahr: | 2018 |
| Verlag: | Elsevier |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia Computer Science |
| Jahrgang/Volume einer Zeitschrift: | 130 |
| DOI: | 10.1016/j.procs.2018.04.038 |
| URL / URN: | https://doi.org/10.1016/j.procs.2018.04.038 |
| Kurzbeschreibung (Abstract): | Affective computing allows machines to simulate and detect emotional states. The most common method is the observation of the face by camera. However, in our increasingly observed society, more privacy-aware methods are worth exploring that do not require facial images, but instead look at other physiological indicators of emotion. In this work we present the Emotive Couch, a sensor-augmented piece of smart furniture that detects proximity and motion of the human body. We present the design rationale and use standard machine learning techniques to detect the three basic emotions Anxiety, Interest, and Relaxation. We evaluate the performance of our approach with 15 participants in a study that includes various affect elicitation methods, achieving an accuracy of 77.7 %. |
| Freie Schlagworte: | Affective computing, Emotion detection, Capacitive proximity sensing |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 10 Jul 2019 08:26 |
| Letzte Änderung: | 10 Jul 2019 08:26 |
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