Deep Leg Tracking by Detection and Gait Analysis in 2D Range Data for Intelligent Robotic Assistants

Abstract

Online human leg tracking and gait analysis are crucial functionalities for mobility assistant robots, like intelligent walkers. Usually, such walkers are equipped with various sensors for the extraction of human-related features for adaptive human-robot interaction and assistance. We treat the gait detection problem jointly, presenting a novel method for detecting and recognizing gait features from 2D range data produced by a laser sensor mounted on a robotic walker. We propose an effective Convolutional Neural Network (CNN) as a powerful feature extractor for detecting the user’s leg centers in range data represented as occupancy grid maps. We couple the CNN with a Long Short Term Memory (LSTM) network for learning the legs’ motion temporal dynamics while walking, improving the prior detection, and providing better leg occlusion handling. Moreover, we perform gait analysis by recognizing gait phases over both legs by feeding the leg tracking output to a subsequent LSTM. Our proposed lightweight framework has been trained and tested on real patients-data. The presented experimental results show our method’s efficiency in providing accurate detections compared to state-of-the-art and application to an online system due to its high frequency, making it a competitive method for gait detection on robotic mobility assistants.