Authors
Mouseli, P., De Vera, N. W., Sagheer, S., Reid, W. D., Jurisica, I., Angst, M. S., Aghaeepour, N., Moayedi, M., Cioffi, I.
Abstract
Self-reported pain remains the clinical standard for assessing musculoskeletal pain severity, but its subjectivity limits objective diagnosis, monitoring, and therapeutic development. We present LENS (Latent sEMG Neuromuscular Signature), which decodes exertion-evoked musculoskeletal pain intensity from surface electromyography (sEMG) alone. LENS was pre-trained via a cross-modal self-supervised objective that reconstructs muscle oxygenation from sEMG in 183 adults. LENS was trained exclusively on healthy muscle physiology and generalized to an independent healthy cohort (Spearman's {rho}=0.52) and discriminated moderate-to-severe pain (AUROC=0.83). Using a label-free test-time adaptation, LENS generalized to an unseen chronic musculoskeletal pain cohort--myogenous temporomandibular disorder (mTMD)--with comparable performance ({rho}=0.61; AUROC=0.86). Reverse transfer, from mTMD to controls, was substantially weaker, an asymmetry attributable to elevated motor variability in chronic pain; excluding highly variable mTMD participants recovered performance, indicating a conserved pain signature masked by pathology-specific motor noise. LENS offers a scalable, physiological correlate of musculoskeletal pain from a single sEMG channel.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 15 Sep 2026.
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