An intelligent socially assistive robot-wearable sensors system for personalized user dressing assistance

摘要

AbstractIndividuals living with cognitive impairments are faced with unique challenges in completing important activities of daily living such as dressing. In this paper, we present the first socially assistive robot-wearable sensors system to provide dressing assistance through social human-robot interactions. A novel robot-wearable architecture has been developed to recognize and classify user dressing actions and provide personalized prompts and feedback. Our system uses smart clothing with embedded strain sensors to estimate the user's actions, which are then classified into different dressing steps. Our assistive robot uses a MAXQ hierarchical learning method to learn appropriate assistive behaviors to aid a user with the sequence of dressing steps. Experiments conducted validated the performance of our robot-wearable system in identifying and effectively responding to a variety of user states and dressing step actions. Furthermore, a robot demonstration study with stakeholders found that overall, they had positive perceptions and attitudes towards the socially assistive robot-wearable system, in particular with respect to its usefulness with the intended user population.KEYWORDS: Socially assistive robotswearable sensorsdeep learningassistance with activities of daily living Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by AGEWELL NCE Inc [AWCRP-2020-12]; Canada Research Chairs: [Natural Sciences and Engineering Research Council]; Natural Sciences and Engineering Research Council of Canada (NSERC) [Grant Number NSERC HeRO CREATE CREATE Fellowship].Notes on contributorsFraser RobinsonFraser Robinson is a MASc candidate in the Department of Mechanical and Industrial Engineering at the University of Toronto.Zinan CenZinan Cen is a MASc candidate in the Department of Mechanical and Industrial Engineering at the University of Toronto.Hani NaguibHani Naguib is a Professor at the University of Toronto, and director of the Toronto Institute for Advanced Manufacturing. His major expertise is in the area of manufacturing of programmable materials including stimuli responsive materials, meta materials, and nanostructured materials. He is a Professional Engineer in Canada, a Chartered Engineer in U.K.Goldie NejatGoldie Nejat is a Full Professor and Associate Chair for Research in the Department of Mechanical and Industrial Engineering at the University of Toronto. She is the Canada Research Chair in Robots for Society and the Founder and Director of the Autonomous Systems and Biomechatronics Laboratory. She is also an Adjunct Scientist at both KITE at the Toronto Rehabilitation Institute, and the Rotman Research Institute at Baycrest Health Sciences.