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

Harshavardhana T Gowda, Neha Kaul, Carlos Carrasco, Marcus Battraw, Safa Amer, Saniya Kotwal, Selena Lam, Zachary D. McNaughton, Ferdous Rahimi, Sana Shehabi, Jonathon Schofield, Lee M. Miller

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.