Automatic discrimination of laughter using distributed sEMG

Abstract

Laughter is a very interesting non-verbal human vocalization. It is classified as a semi voluntary behavior despite being a direct form of social interaction, and can be elicited by a variety of very different stimuli, both cognitive and physical. Automatic laughter detection, analysis and classification will boost progress in affective computing, leading to the development of more natural human-machine communication interfaces. Surface Electromyography (sEMG) on abdominal muscles or invasive EMG on the larynx show potential in this direction, but these kinds of EMG-based sensing systems cannot be used in ecological settings due to their size, lack of reusability and uncomfortable setup. For this reason, they cannot be easily used for natural detection and measurement of a volatile social behavior like laughter in a variety of different situations. We propose the use of miniaturized, wireless, dry-electrode sEMG sensors on the neck for the detection and analysis of laughter. Even if with this solution the activation of specific larynx muscles cannot be precisely measured, it is possible to detect different EMG patterns related to larynx function. In addition, integrating sEMG analysis on a multisensory compact system positioned on the neck would improve the overall robustness of the whole sensing system, enabling the synchronized measure of different characteristics of laughter, like vocal production, head movement or facial expression; being at the same time less intrusive, as the neck is normally more accessible than abdominal muscles. In this paper, we report laughter discrimination rate obtained with our system depending on different conditions.

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“…For example, an ordering system recommends food items to customers from their facial expressions and eye motions [1], and a therapist system infers users' emotions from facial expressions and voices to support their mental states [2]. Previous studies aimed to detect laughter based on facial expressions, voices, and electromyograms related to laughter behaviors, rather than to estimate subjective fun [3]- [6]. In some situations, sensors for measuring these signals are unavailable.…”