Wearable Soft Sensors for Spinal Monitoring: Soft Materials, Multimodal Integration, and Clinical Translation

Spinal musculoskeletal disorders demand continuous assessment of movement and loading, whereas routine clinical imaging provides important but episodic structural information. While wearable soft sensors offer a non-invasive approach to complement these assessments, translating laboratory-scale prototypes into robust clinical systems remains challenging because of electromechanical stability, signal fidelity, and data interpretation. This review critically examines the end-to-end development of soft wearable technologies for spinal monitoring, moving beyond a materials-focused survey to address these translational gaps. First, we evaluate advances in soft sensing mechanisms, highlighting the trade-offs between mechanical compliance and stable sensor performance. We then examine system-level integration, including soft or flexible sensing elements, conventional sensors on compliant substrates, rigid-soft interfaces, and multimodal systems for monitoring posture, motion, pressure, and muscle activity. Furthermore, we discuss signal-processing and artificial intelligence-assisted workflows for denoising and extracting meaningful physiological features. By comparing reported performance metrics and validation settings, we assess representative applications in scoliosis and chronic low back pain. Current evidence demonstrates technical feasibility in laboratory tests and small studies, but does not yet establish broad diagnostic benefit or routine clinical effectiveness. Finally, we identify key evidence gaps, including external validation, patient-based studies, standardized protocols, and long-term assessment, that must be addressed for clinical translation.