Two-Dimensional Deep Convolutional Neural Networks for Estimating Stride Length and Velocity in Institutionalized Older Adults

Extracting stride length and velocity from wearable sensors is traditionally based on the double integration of accelerometer data with zero-velocity update (ZUPT) technique. However, this approach might not be suitable for institutionalized older adults, whose clear zero-velocity phase cannot be detected accurately. While deep learning models have been proposed to overcome this limitation, these approaches need subject-specific labeled data, which are difficult to collect in practice, to calibrate the models. We show that 2-D deep convolutional neural networks (DCNNs) can be used to extract accurate estimates of stride length and velocity with instrumented footwear. Leave-one-subject-out cross-validation is used to avoid overfitting of the results to deep learning models on data collected from {N}={95} institutionalized older adults with two different stride definitions during the 6-min walk test (6MWT). When stride is defined from the initial contact (IC) to the next IC, DCNNs result in better interrater reliability and improved performance by 38.8% and 31.2% relative to conventional techniques (i.e., the double integration method with ZUPT technique) for stride length and velocity, respectively. The performance of DCNNs does not degrade substantially when the stride is defined from the foot-flat (FF) phase (i.e., zero-velocity phase) to the next FF phase. Deep learning models are robust to intersubject variability without requiring subject-specific labeled data, indicating the potential of their use for out-of-the-lab gait analysis. Their performance is independent of stride definitions, making them more suitable for institutionalized older adults than conventional techniques, where ICs can be reliably detected even when they do not occur under the hindfoot.