Low back pain (LBP) remains one of the most prevalent and disabling musculoskeletal conditions worldwide, shaped by interacting mechanical, neurophysiological, inflammatory, vascular, and behavioral factors. Conventional care often relies on generalized exercise programs and episodic, predominantly subjective assessment, which can underrepresent inter-individual heterogeneity and longitudinal change. Recent bioengineering advances enable continuous, multimodal monitoring of objective correlates of function—neuromuscular activation and coordination (sEMG/polyEMG), movement patterns and activity exposure (IMU), and complementary physiological context (e.g., autonomic and perfusion-related signals). Rather than measuring pain directly, these signals can contextualize symptoms, support treatment stratification within non-surgical care, and enable trajectory monitoring with early non-response flags to guide timely rehabilitation adjustment under clinician oversight. When integrated with transparent, implementation-oriented analytics, biosensing can also support incremental closed-loop rehabilitation through patient-facing feedback and adaptive progression rules. This review synthesizes current and emerging biosensing approaches for LBP and highlights key translational requirements—outcome-linked validation, standardization, and workflow integration—to bridge engineering innovation with clinically usable, data-informed rehabilitation.
Gallo et al. (Thu,) studied this question.