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AI enhanced collaborative human-machine interactions for home-based telerehabilitation

Le, Hoang H; Loomes, Martin J; Loureiro, Rui Cv; (2023) AI enhanced collaborative human-machine interactions for home-based telerehabilitation. Journal of Rehabilitation and Assistive Technologies Engineering , 10 10.1177/20556683231156788. Green open access

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Abstract

The use of robots in a telerehabilitation paradigm could facilitate the delivery of rehabilitation on demand while reducing transportation time and cost. As a result, it helps to motivate patients to exercise frequently in a more comfortable home environment. However, for such a paradigm to work, it is essential that the robustness of the system is not compromised due to network latency, jitter, and delay of the internet. This paper proposes a solution to data loss compensation to maintain the quality of the interaction between the user and the system. Data collected from a well-defined collaborative task using a virtual reality (VR) environment was used to train a robotic system to adapt to the users' behaviour. The proposed approach uses nonlinear autoregressive models with exogenous input (NARX) and long-short term memory (LSTM) neural networks to smooth out the interaction between the user and the predicted movements generated from the system. LSTM neural networks are shown to learn to act like an actual human. The results from this paper have shown that, with an appropriate training method, the artificial predictor can perform very well by allowing the predictor to complete the task within 25 s versus 23 s when executed by the human.

Type: Article
Title: AI enhanced collaborative human-machine interactions for home-based telerehabilitation
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/20556683231156788
Publisher version: https://doi.org/10.1177/20556683231156788
Language: English
Additional information: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages
Keywords: collaborative rehabilitation, engagement, haptic device, long-short term memory, motivation, nonlinear autoregressive models with exogenous input, social interaction, telerehabilitation, virtual reality
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Ortho and MSK Science
URI: https://discovery.ucl.ac.uk/id/eprint/10172291
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