Abstract

Reliable hand gesture recognition (HGR) using wearable sensors remains challenging due to variability in arm posture, motion, and individual muscle activation patterns. To facilitate systematic investigation of multi-modal sensing strategies under realistic operating conditions, this paper presents a comprehensive dataset of surface electromyography (sEMG) and pressure-based force myography (pFMG) signals. The dataset includes three complementary subsets acquired under controlled static arm posture, multiple static arm postures, and combined static and dynamic arm postures. Signals were recorded using a custom-designed co-located sEMG-pFMG armband, enabling the simultaneous capture of electrical muscle activation and mechanical muscle deformation. System validation is conducted from three perspectives. First, hardware-level signal quality is assessed through signal-to-noise ratio (SNR) analysis across all gestures and sensing channels, demonstrating stable and reliable signal acquisition. Second, representative raw waveform examples are provided to qualitatively illustrate modality-specific and condition-dependent signal characteristics under static and dynamic arm-posture scenarios. Third, reproducible baseline gesture recognition experiments are performed using conventional machine learning classifiers. By providing multi-modal data acquired under both static and dynamic arm-posture conditions, along with clearly de-fined experimental protocols and baseline benchmarks, this dataset serves as a valuable resource for developing, evaluating, and comparing gesture recognition algorithms and arm-wearable human–machine interface (HMI) systems.

IPC Classification

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