Spatial or Temporal Signal Considered for Gait Recognition based on Optic-fiber Sensor.

Gait recognition plays a significant role in the realm of wearable robotics, particularly in facilitating the development of sensors for gait analysis in rehabilitation. In this study, we proposed a novel optic-based sensor for gait recognition and employed four neural network models to extract both temporal and spatial information. Through a comparative analysis of these neural network models across the gait cycle, we investigate the implications of different sensor placement locations and the contributions made by diverse types of information in the context of gait analysis. Through all the models, RNN performed the highest accuracy when the sensor was fixed on the thigh or the calf, and temporal convolutional capsule network(TCCN) performed better when the sensors were fixed both on the thigh and the calf. The results suggested that temporal information could be mainly considered during gait cycle when a single sensor was fixed, and the combination of both temporal and spatial information is critical for accurate gait recognition for multiple sensors placement. This work can provide a new idea for the chosen of recognition models with different sensor placement patterns.