Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology · 2026
Sex, age, and race representation in electromyography research: A cross-sectional methodological survey.
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Li Bingle, Tang Xinrui, Xu Junbo, Aranceta-Garza Alejandra
Аннотация
BACKGROUND: Surface electromyography (sEMG) is widely utilized in biomechanics and human-computer interaction. Modern sEMG systems rely on machine learning models , whose generalization heavily depends on physiological variations linked to sex, age, and ancestry, though these are often unexplored.
OBJECTIVE: This survey critically appraises how demographic variables (sex, age, race) are reported in primary sEMG studies and quantifies cohort diversity at both aggregate and individual-study levels.
METHODS: We analyzed a stratified random sample of 377 sEMG articles (January 2000-April 2026) from PubMed and IEEE Xplore. A validated large-language-model workflow extracted demographic data to compute reporting rates, gender imbalance indices, and age spans.
RESULTS: Overall reporting rates were 87.5% for gender, 89.9% for age, and 2.6% for race. Despite macroscopic sex balance, 49.5% of individual studies exhibited severe gender imbalance. Furthermore, cohorts overwhelmingly concentrated on young adults (19-35 years) with narrow age bands, revealing pronounced homogenization.
CONCLUSION: These findings underscore a critical demographic blind spot in current sEMG research. To ensure the safety, robustness, and true generalizability of intelligent neural interfaces across heterogeneous real-world populations, there is an urgent need to mandate structured demographic reporting standards and explicitly define the fairness boundaries of algorithm models.
OBJECTIVE: This survey critically appraises how demographic variables (sex, age, race) are reported in primary sEMG studies and quantifies cohort diversity at both aggregate and individual-study levels.
METHODS: We analyzed a stratified random sample of 377 sEMG articles (January 2000-April 2026) from PubMed and IEEE Xplore. A validated large-language-model workflow extracted demographic data to compute reporting rates, gender imbalance indices, and age spans.
RESULTS: Overall reporting rates were 87.5% for gender, 89.9% for age, and 2.6% for race. Despite macroscopic sex balance, 49.5% of individual studies exhibited severe gender imbalance. Furthermore, cohorts overwhelmingly concentrated on young adults (19-35 years) with narrow age bands, revealing pronounced homogenization.
CONCLUSION: These findings underscore a critical demographic blind spot in current sEMG research. To ensure the safety, robustness, and true generalizability of intelligent neural interfaces across heterogeneous real-world populations, there is an urgent need to mandate structured demographic reporting standards and explicitly define the fairness boundaries of algorithm models.