Objective assessment of lower-limb muscular strength typically requires maximal-effort testing or laboratory-based equipment, limiting practicality in routine exercise and rehabilitation settings. Wearable exoskeletons provide controlled resistance and quantitative biomechanical monitoring, offering a potential platform for structured strength evaluation. This study proposes an interpretable framework for estimating lower-limb strength using joint torque and surface electromyography (sEMG) metrics obtained during exoskeleton-assisted exercise.

Thirty healthy adults completed conventional strength assessments to construct a composite reference index. Participants then performed guided squat, knee-up, and lunge exercises using a hip-joint exoskeleton. Torque and sEMG signals were used to derive execution-based performance metrics. Decision-tree models were developed for three-level strength classification, and regression models were constructed for quantitative strength estimation.

Metrics reflecting exercise execution consistency, particularly guided exercise pace, emerged as the most influential indicators of strength. The expert rule achieved precision up to 0.95 for strength classification, and regression models showed strong association with the composite strength index (maximum   ,   ).

The proposed framework enables interpretable classification and quantitative estimation of lower-limb strength using standardized wearable exercise. Integration of torque and sEMG-derived metrics supports practical and data-driven strength assessment without reliance on maximal-effort testing.