Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation.
Introduction Occupational rehabilitation often involves functional capacity evaluations (FCE) that use simulated work tasks to assess work ability. Currently, there exists no single, streamlined solution to simulate all or a large number of standard work tasks. Such a system would improve FCE and fu...
| Published in: | Journal of Occupational Rehabilitation Vol. 30; no. 3; pp. 362 - 371 |
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| Main Authors: | , , , |
| Format: | pictorial review Journal Article |
| Published: |
Springer Nature
Sep2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144951465&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144951465 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10530487 JOR jtl: Journal of Occupational Rehabilitation issn: 10530487 maglogo: N pubinfo: dt: Sep2020 vid: 30 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 144951465 144048056 144951465 144951465 10.1007/s10926-020-09888-w 144951465 ppf: 362 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation. aug: au: Fong, Jason Ocampo, Renz Gross, Douglas P. Tavakoli, Mahdi affil: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada sug: subj: Robotics Machine Learning Algorithms Work Capacity Evaluation Methods Rehabilitation, Vocational Methods Quality Improvement Musculoskeletal Diseases Rehabilitation Worker's Compensation Occupational-Related Injuries Diffusion of Innovation Occupational Therapists User-Computer Interface ab: Introduction Occupational rehabilitation often involves functional capacity evaluations (FCE) that use simulated work tasks to assess work ability. Currently, there exists no single, streamlined solution to simulate all or a large number of standard work tasks. Such a system would improve FCE and functional rehabilitation through simulating reaching maneuvers and more dexterous functional tasks that are typical of workplace activities. This paper reviews efforts to develop robotic FCE solutions that incorporate machine learning algorithms. Methods We reviewed the literature regarding rehabilitation robotics, with an emphasis on novel techniques incorporating robotics and machine learning into FCE. Results Rehabilitation robotics aims to improve the assessment and rehabilitation of injured workers by providing methods for easily simulating workplace tasks using intelligent robotic systems. Machine learning-based approaches combine the benefits of robotic systems with the expertise and experience of human therapists. These innovations have the potential to improve the quantification of function as well as learn the haptic interactions provided by therapists to assist patients during assessment and rehabilitation. This is done by allowing a robot to learn based on a therapist's motions ("demonstrations") what the desired workplace activity ("task") is and how to recreate it for a worker with an injury ("patient"). Through Telerehabilitation and internet connectivity, these robotic assessment techniques can be used over a distance to reach rural and remote locations. Conclusions While the research is in the early stages, robotics with integrated machine learning algorithms have great potential for improving traditional FCE practice. pubtype: Academic Journal doctype: pictorial review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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