Trajectory-based gait pattern shift detection for assistive robotics applications

In the medical field, rehabilitation of the lower limbs is a slow and continuous process, where healthcare professionals have to follow each patient over long periods of time. In conventional rehabilitation, the progression of a patient is assessed by a professional, who analyzes visual tracking data. This assessment is dependent on each professional’s own experience. In this paper, we propose an approach to analyze tracking data, captured by our robotic walker’s gait tracking system, to detect shifts in the gait pattern over time automatically. For this purpose, the system takes in gait tracking data and segments it into gait cycles (heel strike to heel strike). Then, our approach handles each gait cycle considering it as a group of gait parameters that define trajectories in that time frame. From each gait parameter within the cycle, spatiotemporal features are extracted and similarity rates are computed using autoencoders. These spatiotemporal features and similarity rates are fused in a feature space which is fed to a one-class support vector machine that constructs a model of the observable gait cycle. Each posterior observed gait cycle is checked for shifts, using a set of proposed novelty detection techniques. Experimental tests using a dataset captured using our robotic walker platform revealed a promising performance when detecting if a gait cycle is ‘reference’ or ‘novel’ when compared to a previously trained model of a unique ‘reference’ gait pattern.