A database of upper limb surface electromyogram signals from demographically diverse individuals

TL;DR

This paper introduces a comprehensive EMG dataset from diverse individuals to study demographic effects on gesture decoding and to develop fair, unbiased neuromuscular interfaces.

Contribution

It provides a new, publicly available dataset of upper limb EMG signals from diverse demographics, including physiological measures, to facilitate research on demographic confounds and algorithm fairness.

Findings

Dataset includes 91 diverse adults with physiological measures.

State-of-the-art gesture decoding validates data quality.

Benchmark for fair and unbiased EMG-based gesture decoding.

Abstract

Upper limb based neuromuscular interfaces aim to provide a seamless way for humans to interact with technology. Among noninvasive interfaces, surface electromyogram (EMG) signals hold significant promise. However, their sensitivity to physiological and anatomical factors remains poorly understood, raising questions about how these factors influence gesture decoding across individuals or groups. To facilitate the study of signal distribution shifts across individuals or groups of individuals, we present a dataset of upper limb EMG signals and physiological measures from 91 demographically diverse adults. Participants were selected to represent a range of ages (18 to 92 years) and body mass indices (healthy, overweight, and obese). The dataset also includes measures such as skin hydration and elasticity, which may affect EMG signals. This dataset provides a basis to study demographic confounds in EMG signals and serves as a benchmark to test the development of fair and unbiased algorithms that enable accurate hand gesture decoding across demographically diverse subjects. Additionally, we validate the quality of the collected data using state-of-the-art gesture decoding techniques.