Short-range gait pattern analysis for potential applications on assistive robotics

摘要

In this paper we propose a gait pattern analysis system that uses stereo vision and machine learning techniques for robotic walker applications. This work contributes with a user monitoring system, that allows the development of more user-centered approaches, such as safer and adaptive HMIs. It also provides a tool to help healthcare personnel in medical assessments. The gait analysis system presented in this paper takes advantage of a stereo vision-based sensor, mounted onboard a robotic walker, to model the user's gait pattern by applying a weighted kernel-density estimator to the captured data. Features are then extracted using a sliding temporal window and classified into one of the trained gait patterns. We have performed experiments both to validate the proposed gait pattern classification system and also to validate its usability. The results obtained from the different experiments evidenced a satisfactory system's performance.