This paper introduces an algorithm for automatically measuring impulsivity. This can be used as a major expressive movement feature in the development of systems for realtime analysis of emotion expression from human full-body movement, a research area which has received increased attention in the affective computing community. In particular, our algorithm is developed in the framework of the EUH2020- ICT Project DANCE aiming at investigating techniques for sensory substitution in blind people, in order to enable perception of and participation in non-verbal, artistic whole-body experiences. The algorithm was tested by applying it to a reference archive of short dance performances. The archive includes a collection of both impulsive and fluid movements. Results show that our algorithm can reliably distinguish impulsive vs. sudden performances.
Automated detection of impulsive movements in HCI / Niewiadomski, Radoslaw; Mancini, Maurizio; Volpe, Gualtiero; Camurri, Antonio. - ELETTRONICO. - (2015), pp. 166-169. (Intervento presentato al convegno 11th Biannual Conference of the ACM SIGCHI Italian Chapter, CHItaly 2015 tenutosi a Roma, Italia nel 2015) [10.1145/2808435.2808466].
Automated detection of impulsive movements in HCI
NIEWIADOMSKI, RADOSLAW;
2015-01-01
Abstract
This paper introduces an algorithm for automatically measuring impulsivity. This can be used as a major expressive movement feature in the development of systems for realtime analysis of emotion expression from human full-body movement, a research area which has received increased attention in the affective computing community. In particular, our algorithm is developed in the framework of the EUH2020- ICT Project DANCE aiming at investigating techniques for sensory substitution in blind people, in order to enable perception of and participation in non-verbal, artistic whole-body experiences. The algorithm was tested by applying it to a reference archive of short dance performances. The archive includes a collection of both impulsive and fluid movements. Results show that our algorithm can reliably distinguish impulsive vs. sudden performances.File | Dimensione | Formato | |
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