Analysis of Muscle Activation Patterns as Indicators of Gestures’ Ease of Use

Abstract

Hand-based gestures are widely used in human-computer interaction but can cause muscle fatigue and discomfort that undermine long-term usability. This exploratory study examined whether gestures' ease of use can be reflected in distinct muscle activation patterns derived from surface electromyography (sEMG) signals. Twenty participants performed 32 predefined gestures while sEMG was recorded from eight forearm and upper-arm muscles, and rated each gesture's perceived effort on a 7-point scale. Gestures were grouped into high- and low-ease-of-use categories, and activation features were compared between groups. Results showed that high-ease-of-use gestures had significantly greater biceps activation, lower extensor carpi radialis activation, and reduced co-activation between the flexor carpi ulnaris and flexor digitorum superficialis. These findings suggest that ease of use relates to reduced motor complexity and reliance on proximal muscles, and demonstrate promise for sEMG in objectively indexing gestures' ease of use, offering insights to inform the design of more usable gesture-based interactions.