Simultaneous Recognition of Hand Gestures, Grasping Force, and Hand Aperture Using a Wearable Displacement Myography System

Monitoring forearm muscle activity is essential for estimating human intention in different applications such as virtual reality, rehabilitation, and prosthetics. While prior studies have mainly focused on recognising gestures or simultaneously classifying gestures and force, predominantly using surface electromyography (sEMG), the simultaneous classification of gesture, force, and hand aperture within the same gesture has not yet been investigated. This work extends previous approaches by evaluating a wearable Displacement Myography (DMG)-based system for the combined recognition of hand gestures, four grasping force levels, and multiple hand aperture levels, and by directly benchmarking it against an sEMG system under identical experimental conditions. Data from 13 healthy participants were collected using the MyoLog armband with DMG sensors and sEMG sensors while performing three grasp types (pinch, key, power) with objects of varying size and four force levels, generating 33 configurations. Across several classifiers, the DMG system achieved a peak accuracy of 93 ± 2%, significantly outperforming EMG (64 ± 10%). DMG showed superior discrimination of hand aperture levels (98 ± 3%), while EMG showed relatively better but still lower performance in force-level classification (82 ± 11%). The results are supported by separability analysis using the Fisher Discriminant Ratio, which demonstrated consistently higher class separability for DMG. These findings highlight the suitability of DMG for applications requiring precise recognition of hand aperture and force levels, offering a compact, computationally efficient, and stimulation-compatible solution that overcomes several limitations of EMG-based systems.