Bias in Surface Electromyography Features across a Demographically Diverse Cohort

2026-04-15

AI Summary

This study addresses the inconsistent performance of surface electromyography (sEMG)-based human-machine interfaces across diverse populations, which may stem from demographic influences on sEMG features. For the first time, it systematically quantifies associations between 147 commonly used sEMG features and demographic variables—including age, sex, and body mass index—using gesture data collected from 81 demographically diverse participants. Combining linear mixed-effects modeling with partial least squares regression, the analysis reveals that 33% (49 out of 147) of the features exhibit significant demographic bias. These findings highlight a potential source of systematic unfairness in current decoding approaches and provide critical evidence for developing fair, generalizable neural interfaces capable of robust performance across heterogeneous user populations.

Technology Category

Machine Learning: Ethics, Bias, and FairnessHumans and AI: Intelligent User InterfacesNatural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

User Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingSocial Networks and Social Media: Fairness and bias in social network and social media analysisEconomics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

Neuromotor decoding from upper-limb electromyography (sEMG) can enhance human-machine interfaces and offer a more natural means of controlling prosthetic limbs, virtual reality, and household electronics. Unfortunately, current sEMG technology does not always perform consistently across users because individual differences such as age and body mass index, among many others, can substantially alter signal quality. This variability makes sEMG characteristics highly idiosyncratic, often necessitating laborious personalization and iterative tuning to achieve reliable performance. This variability has particular import for sEMG-based assistive devices and neural interfaces, where demographic biases in sEMG features could undermine broad and fair deployment. In this study, we explore how demographic differences affect the sEMG signals produced and their implications for machine learning-based gesture decoding. We analyze the data set provided by, in which we derive 147 common sEMG features extracted from 81 demographically diverse individuals performing discrete hand gestures. Using mixed-effects linear models and partial least squares (PLS) analysis, which take into consideration demographic variables (including age, sex, height, weight, skin properties, subcutaneous fat, and hair density), we identify that 33\% (49 of 147) of commonly used sEMG features show significant associations with demographic characteristics. These results may help guide the development of fair and unbiased sEMG-based neural interfaces across a diverse population.

Problem

• surface electromyography
• demographic bias
• neuromotor decoding
• human-machine interface
• feature variability

Innovation

• demographic bias
• surface electromyography (sEMG)
• neuromotor decoding
• mixed-effects models
• fair neural interfaces