HMM-based Pathological Gait Analyzer for a User-Adaptive Intelligent Robotic Walker

During the past decade, robotic technology has evolved considerably towards the development of cognitive robotic systems that enable close interaction with humans. Application fields of such novel robotic technologies are now wide spreading covering a variety of human assistance functionalities, aiming in particular at supporting the needs of human beings experiencing various forms of mobility or cognitive impairments. Mobility impairments are prevalent in the elderly population and constitute one of the main causes related to difficulties in performing Activities of Daily Living (ADLs) and consequent reduction of quality of life. This paper reports current research work related to the development of a pathological gait analyzer for intelligent robotic rollator aiming to be an input to a user-adaptive and context-aware robot control architecture. Specifically, we present a novel method for human leg tracking using Particle Filters and Probablistic Data Association from a laser scanner, constituting a nonwearable and non-intrusive approach. The tracked positions and velocities of the user’s legs are the observables of an HMM, which provides the gait phases of the detected gait cycles. Given those phases we compute specific gait parameters, which are used for medical diagnosis. The results of our pathological gait analyzer are validated using ground truth data from a GAITRite system. The results presented in this paper demonstrate that the proposed human data analysis scheme has the potential to provide the necessary methodological (modeling, inference, and learning) framework for a cognitive behaviorbased robot control system.