LAUSR

Monitoring the changes in gait patterns is important to individuals’ health. Gait analysis should be taken as early as possible to prevent gait impairments and improve gait quality. Accurate stride-length estimation and gait rehabilitation activity recognition are fundamental components in gait monitoring, gait analysis, and long-term gait care. This article proposes a novel multimodality deep learning architecture to investigate the applications of stride length (SL) estimation and rehabilitation activity recognition. In order to verify this architecture, we have conducted the data collection and data labeling with our customized wearable sensing system. The sensing system can provide sensor readings from 96 sensors-based pressure array and 3-channels accelerometer and gyroscope. Many experiments with multiple perspective analysis are implemented to evaluate the models’ precision, robustness, and reliability. The multimodality deep learning architecture can map multiple sensor readings to the resulting SL with a mean absolute error of 3.89 cm and accurately detect the gait activity with an accuracy of 97.08%. It correlates the step length estimation and gait activity recognition to fulfill comprehensive long-term gait information statistic. The proposed applications’ implementation enriched our previous gait study and brought insights for clinically relevant wearable gait monitoring and gait analysis.