Intelligent multimodal sensor fusion for early knee disorder detection and injury prevention using prosthetic gait control

Wearable systems for knee pathology detection and prosthetic control remain constrained by diagnostic limitations or rigid actuation. This study introduces an integrated two-phase framework combining non-invasive screening with adaptive prosthetic control. Phase 1 employs novel time-frequency features (Enhanced Mean Absolute Value/Enhanced Wavelength), achieving 94.7% abnormality detection accuracy <i>via</i> Extra Trees classifier, <i>a</i> + 3.16% improvement over conventional features, which is validated through 10-fold cross-validation and rigorous statistical testing (Friedman/Nemenyi, 95% confidence intervals). SHAP analysis yields clinician-interpretable thresholds (e.g. Semitendinosus EMAV > 0.3 mV). Phase 2 utilises multimodal fusion (EMG, FSR, IMU) to achieve 99.2% gait phase accuracy with XGBoost, enabling real-time health-adaptive prosthetic control that dynamically modulates: phase-transition timing (400 ms abnormal vs. 300 ms normal), EMG thresholds (0.15 mV vs. 0.10 mV), and motor gains (2.5× vs. 1.0×) based on pathology status. Validated in a LabVIEW-based control environment across variable terrains and speeds, this end-to-end diagnostics-to-control implementation delivers superior screening accuracy (>4.7% gain vs. deep learning) while enabling context-aware prosthetic adaptation, establishing a new paradigm for accessible musculoskeletal rehabilitation.