Artificial-Intelligence-Powered Lower Limb Assistive Devices: Future of Home Care Technologies

Abstract

Healthcare systems are burdened by mobility impairments resulting from aging and neurological conditions. One of the recent advances in robotics is lower limb assistive/rehabilitative devices that can make independent living possible. Nonetheless, some limitations need to be addressed before robotics can be used in home-based applications. This paper describes the current state of the art in intelligent motion planning and control of lower limb assistive devices, which have addressed some of these challenges. Adaptable central pattern generators and the divergent component of motion are introduced as methods for personalized motion planning based on physical human–robot interaction (pHRI). Uncertainty analysis for neural networks is introduced to increase safety in motion planning based on pHRI. For the case that a user cannot apply physical interaction, a reinforcement-learning-based approach is introduced to switch between different modes of walking based on the user's input via a push button embedded in a walker. Moreover, a smart walker is introduced as a device that can be synchronized with the lower limb exoskeleton to assist users with their daily activities. Also, a roadmap for future steps that can make lower limb assistive/rehabilitative devices a good fit for home use is introduced.