Neural network to predict knee medial joint contact force from custom instrumented insole

A method for training a neural network model to determine medial joint contact force, the method comprising:

• obtaining a plurality of training data comprising sets of corresponding force plate data, insole sensor data, and motion capture data, wherein the force plate data, insole sensor data, and motion capture data of each set was acquired simultaneously from a monitored gait;
• extracting a plurality of heel strike to toe-off time periods from the plurality of training data;
• assigning a gait categorization label corresponding to the plurality of training data;
• determining a ground reaction force, a plurality of knee moments, and a plurality of knee angles based on said force plate data and motion capture data;
• determining a plurality of joint contact forces at a plurality of time points during the plurality of heel strike to toe-off time periods;
• providing an input training array to the neural network, the input training array comprising insole sensor data corresponding to the plurality of heel strike to toe-off time periods;
• providing an output training array to the neural network, the output training array comprising the plurality of joint contact forces corresponding to the plurality of heel strike to toe-off periods;
• training the neural network model to output a predicted joint contact force value corresponding to a calculated joint contact force measurement from force plate data, based only on insole data;
• and
• storing the trained neural network model after it has been validated.