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 manuscript using these models and data is currently in review at IEEE Transactions on Neural Systems & Rehabilitation Engineering (TNSRE):

Miller, D.E., Brown, A.E., Bianco, N.A., Delp, S.L., Collins, S.H., n.d. A data-driven approach to estimate changes in peak knee contact force with exoskeleton assistance.