Task selection for a sensor-based, wearable, upper limb training device for stroke survivors: A multi-stage approach
Task selection for a sensor-based, wearable, upper limb training device for stroke survivors: A multi-stage approach
Purpose: Post-stroke survivors report that feedback helps to increase training motivation. A wearable system (M-MARK), comprising movement and muscle sensors and providing feedback when performing everyday tasks was developed. The objective reported here was to create an evidence-based set of upper-limb tasks for use with the system. Materials and methods: Data from two focus groups with rehabilitation professionals, ten interviews with stroke survivors and a review of assessment tests were synthesized. In a two-stage process, suggested tasks were screened to exclude non-tasks and complex activities. Remaining tasks were screened for suitability and entered into a categorization matrix. Results: Of 83 suggestions, eight non-tasks, and 42 complex activities were rejected. Of the remaining 33 tasks, 15 were rejected: five required fine motor control; eight were too complex to standardize; one because the role of hemiplegic hand was not defined and one involved water. The review of clinical assessment tests found no additional tasks. Eleven were ultimately selected for testing with M-Mark. Conclusions: Using a task categorization matrix, a set of training tasks was systematically identified. There was strong agreement between data from the professionals, survivors and literature. The matrix populated by tasks has potential for wider use in upper-limb stroke rehabilitation. IMPLICATIONS FOR REHABILITATION Rehabilitation technologies that provide feedback on quantity and quality of movements can support independent home-based upper limb rehabilitation. Rehabilitation technology systems require a library of upper limb tasks at different levels for people with stroke and therapists to choose from. A user-defined and evidence-based set of upper limb tasks for use within a wearable sensor device system have been developed.
Stroke, arm rehabilitation, functional tasks, task categorization, wearable sensors
Turk, Ruth
9bb21965-6f9f-4c9c-8505-94df8e168f52
Whitall, Jill
9761aefb-be80-4270-bc1f-0e726399376e
Meagher, Claire
fd95d905-6d0a-46b8-b986-9c700ee9b9e1
Stokes, Maria
71730503-70ce-4e67-b7ea-a3e54579717f
Roberts, Sue
99b64a85-e0b9-47fb-a88b-db24a3561ed7
Woodham, Sasha
12713848-b86f-41b1-8cef-15db0f91a41e
Burridge, Jane
0110e9ea-0884-4982-a003-cb6307f38f64
Clatworthy, Philip
4efb077e-f2b2-47f0-8bb5-24b42461f076
27 April 2022
Turk, Ruth
9bb21965-6f9f-4c9c-8505-94df8e168f52
Whitall, Jill
9761aefb-be80-4270-bc1f-0e726399376e
Meagher, Claire
fd95d905-6d0a-46b8-b986-9c700ee9b9e1
Stokes, Maria
71730503-70ce-4e67-b7ea-a3e54579717f
Roberts, Sue
99b64a85-e0b9-47fb-a88b-db24a3561ed7
Woodham, Sasha
12713848-b86f-41b1-8cef-15db0f91a41e
Burridge, Jane
0110e9ea-0884-4982-a003-cb6307f38f64
Clatworthy, Philip
4efb077e-f2b2-47f0-8bb5-24b42461f076
Turk, Ruth, Whitall, Jill, Meagher, Claire, Stokes, Maria, Roberts, Sue, Woodham, Sasha, Burridge, Jane and Clatworthy, Philip
(2022)
Task selection for a sensor-based, wearable, upper limb training device for stroke survivors: A multi-stage approach.
Disability and Rehabilitation.
(doi:10.1080/09638288.2022.2065542).
Abstract
Purpose: Post-stroke survivors report that feedback helps to increase training motivation. A wearable system (M-MARK), comprising movement and muscle sensors and providing feedback when performing everyday tasks was developed. The objective reported here was to create an evidence-based set of upper-limb tasks for use with the system. Materials and methods: Data from two focus groups with rehabilitation professionals, ten interviews with stroke survivors and a review of assessment tests were synthesized. In a two-stage process, suggested tasks were screened to exclude non-tasks and complex activities. Remaining tasks were screened for suitability and entered into a categorization matrix. Results: Of 83 suggestions, eight non-tasks, and 42 complex activities were rejected. Of the remaining 33 tasks, 15 were rejected: five required fine motor control; eight were too complex to standardize; one because the role of hemiplegic hand was not defined and one involved water. The review of clinical assessment tests found no additional tasks. Eleven were ultimately selected for testing with M-Mark. Conclusions: Using a task categorization matrix, a set of training tasks was systematically identified. There was strong agreement between data from the professionals, survivors and literature. The matrix populated by tasks has potential for wider use in upper-limb stroke rehabilitation. IMPLICATIONS FOR REHABILITATION Rehabilitation technologies that provide feedback on quantity and quality of movements can support independent home-based upper limb rehabilitation. Rehabilitation technology systems require a library of upper limb tasks at different levels for people with stroke and therapists to choose from. A user-defined and evidence-based set of upper limb tasks for use within a wearable sensor device system have been developed.
Text
Task selection for a sensor based wearable upper limb training device for stroke survivors a multi stage approach (1)
- Version of Record
More information
Accepted/In Press date: 9 April 2022
e-pub ahead of print date: 25 April 2022
Published date: 27 April 2022
Additional Information:
Publisher Copyright:
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Keywords:
Stroke, arm rehabilitation, functional tasks, task categorization, wearable sensors
Identifiers
Local EPrints ID: 456955
URI: http://eprints.soton.ac.uk/id/eprint/456955
ISSN: 0963-8288
PURE UUID: 4009112b-e56d-4b0d-96bc-2ed010f5147b
Catalogue record
Date deposited: 18 May 2022 16:48
Last modified: 17 Mar 2024 02:59
Export record
Altmetrics
Contributors
Author:
Jill Whitall
Author:
Claire Meagher
Author:
Sue Roberts
Author:
Sasha Woodham
Author:
Philip Clatworthy
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
