A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton Assistance

Abstract

Lower-limb exoskeletons could benefit individuals with knee osteoarthritis by reducing knee loading. Real-time estimation of knee loads could accelerate the development of load-reducing exoskeletons. However, measuring or estimating internal knee forces remains challenging due to the rarity of force-sensing knee implants and complexity of simulation-based methods. We developed two data-driven models to separately estimate the peaks in knee contact force during early and late stance using a limited set of features from electromyography (EMG), ground reaction force (GRF), and knee angle recordings. These models were trained on experimental data from healthy young adults (N = 6) walking with a wide range of knee-ankle exoskeleton torque assistance conditions. Peak knee contact forces were obtained from EMG-informed musculoskeletal simulations in OpenSim Moco. The data-driven models were evaluated using leave-one-subject-out cross validation on their ability to accurately compare exoskeleton assistance conditions. The data-driven models identified directional changes in peak knee contact force larger than 0.1 body weights (BW) with 90% accuracy for early-stance peak and 79% accuracy for late-stance peak. Both models included GRF and knee angle features, but EMG features reflected phase-specific muscle activity: quadriceps appeared in the early-stance model, plantar flexors in late stance, and hamstrings in both. We developed a simple method to rapidly estimate changes in peak knee contact force. This approach is suitable for systematic interventions that aim to reduce knee load, such as human-in-the-loop optimization of exoskeleton assistance.