Using Transparent Neural Networks and Wearable Inertial Sensors to Generate Physiologically-Relevant Insights for Gait.

Neural networks have been successfully applied to a wide range of human motion analysis topics in combination with wearable sensor data. However, their computation process is not readily comprehensible. Alternatively, many of the model interpretation efforts do not provide physiologically-relevant insights, thus still limiting their use in clinical settings. In this work, we take gait modifications under fatigue and cognitive task performance as a use case to present how in-depth investigations of neural networks can be performed using wearable sensor data. We collected walking data from 16 young healthy individuals in unfatigued and fatigued states and under single- (walking only) and dual-task (walking while concurrently performing a cognitive task) conditions using inertial measurement units. Convolutional neural networks were able to identify both fatigue and dual-task gait patterns with high classification accuracy. To interpret the model, the importance of each time step in the input time series was visualized using Layer-wise Relevance Propagation. The visualization revealed highly individualized gait changes among participants, as well as changes at precise time steps of the input signal that allow further investigations to infer potential underlying mechanisms. Our methods enable in-depth analysis of human movement using transparent neural networks with data collected from unobtrusive, mobile wearable sensors.