Real-Time Gait Event Detection for a Lower Extremity Exoskeleton Robot by Infrared Distance Sensors

Abstract

Gait event detection is a crucial part in the motion control and performance evaluation of exoskeleton robots. To detect the whole gait events conveniently and effectively, a wearable system based on infrared distance sensors is proposed. The heel and toe clearances were used for the first time for the online detection of all six gait events in one gait cycle. Smart shoes integrated with three infrared distance sensors were designed to obtain stable distance signals, which were then converted into effective foot posture information. Moreover, an online detection algorithm was proposed using local search windows and fixed thresholds, resulting in a minimal time delay and a small computational burden. By comparing the system with the motion capture system at different walking speeds, the average detection error of gait events is within 34ms, and the average detection rate of the system is 99.62%. When applied to a lower extremity exoskeleton robot, the system successfully detects all gait events of the human-robot synchronous walk. The results show that the system has good real-time performance and high detection rate. This study provides a convenient and reliable detection method for gait research of human body and exoskeleton robots.