Strike index estimation using a convolutional neural network with a single, shoe-mounted inertial sensor

Abstract

Strike index is a measurement of the center of pressure position relative to the foot length, and it is regarded as a gold standard in classifying strike pattern in runners. However, strike index requires sophisticated laboratory equipment, e.g., force plates and optical motion capture. We present a method of estimating strike index using data from a shoe-mounted inertial measurement unit (IMU) analyzed by a participant-independent convolutional neural network (CNN), which consists of convolutional, max-pooling, and fully-connected layers. To promote data variability, 16 participants were required to land with three strike patterns (rearfoot, midfoot, and forefoot strike) while running on an instrumented treadmill in four conditions i.e., two footwear types and two running speeds. Using the proposed approach, strike index was estimated with a root mean square error of 6.9% and a R2 of 0.89. Training and testing the model with different variations of the data collected showed that the model was robust to changes in speed. The proposed approach enables accurate estimation of strike index outside of traditional gait laboratories. This solution potentially improves running performance and reduces injury risk in distance runners.