Bias in Surface Electromyography Features across a Demographically Diverse Cohort

ArXi:2604.14460v1 Announce Type: cross Neuromotor decoding from upper-limb electromyography (sEMG) can enhance human-machine interfaces and offer a 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.