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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
Task selection for a sensor-based, wearable, upper limb training device for stroke survivors: a multi-stage approach
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. 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. 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. 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 REHABILITATIONRehabilitation 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. 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.
Figshare
Turk, Ruth
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Whitall, Jill
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Meagher, Claire
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Stokes, Maria
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Roberts, Sue
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Woodham, Sasha
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Clatworthy, Philip
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Burridge, Jane
0110e9ea-0884-4982-a003-cb6307f38f64
Turk, Ruth
985d334a-d337-48a6-a112-0665b82d78aa
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
Clatworthy, Philip
4efb077e-f2b2-47f0-8bb5-24b42461f076
Burridge, Jane
0110e9ea-0884-4982-a003-cb6307f38f64

(2022) Task selection for a sensor-based, wearable, upper limb training device for stroke survivors: a multi-stage approach. Figshare doi:10.6084/m9.figshare.19668808.v1 [Dataset]

Record type: Dataset

Abstract

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. 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. 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. 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 REHABILITATIONRehabilitation 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. 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.

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More information

Published date: 1 January 2022

Identifiers

Local EPrints ID: 456804
URI: http://eprints.soton.ac.uk/id/eprint/456804
PURE UUID: 270ea3d6-8e22-4fbc-be38-e6b24ba4ab70
ORCID for Maria Stokes: ORCID iD orcid.org/0000-0002-4204-0890
ORCID for Jane Burridge: ORCID iD orcid.org/0000-0003-3497-6725

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Date deposited: 11 May 2022 16:49
Last modified: 06 May 2023 01:39

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Contributors

Contributor: Ruth Turk
Contributor: Jill Whitall
Contributor: Claire Meagher
Contributor: Maria Stokes ORCID iD
Contributor: Sue Roberts
Contributor: Sasha Woodham
Contributor: Philip Clatworthy
Contributor: Jane Burridge ORCID iD

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