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Title: Stroke Patients’ Acceptance of a Smart Garment for Supporting Upper Extremity Rehabilitation
Authors: Wang, Qi
Chen, Wei
Jia, Jie
Ding, Li
Xiong, Li
Rong, Jifeng
Markopoulos, Panos
Issue Date: 2018
Source: IEEE Journal of Translational Engineering in Health and Medicine, 6, (ART NO°2101009)
Abstract: Objective: The objective is to evaluate to which extent that Zishi a garment equipped with sensors that can support posture monitoring can be used in upper extremity rehabilitation training of stroke patients. Method: 17 stroke survivors (mean age: 55 years old, SD =13.5) were recruited in three hospitals in Shanghai. Patients performed 4 tasks (analytical shoulder flexion, functional shoulder flexion placing a cooking pot, analytical flexion in the scapular plane, functional flexion in the scapular plane placing a bottle of water) with guided feedback on a tablet that was provided through inertial sensors embedded in the Zishi system at the scapula and the thoracic spine region. After performing the training tasks patients completed four questionnaires for assessing their motivation, their acceptance of the system, its credibility and usability. Results: The study participants were highly motivated to train with Zishi and the system was rated high usability, while the subjects had moderate confidence with technology supported training in comparison to the training with therapists. Conclusions: Patients respond positively to using Zishi to support rehabilitation training in a clinical setting. Further developments need to address more on engaging and adaptive feedback. Clinical Impact: This study paves the way for larger scale effectiveness studies.
Notes: Wang, Q (reprint author), Tongji Univ, Coll Design & Innovat, Shanghai 200092, Peoples R China.
Keywords: Wearable system; Stroke; Rehabilitation; Smart garment; Compensatory movement
Document URI:
ISSN: 2168-2372
e-ISSN: 2168-2372
DOI: 10.1109/JTEHM.2018.2853549
ISI #: 000451889200001
Rights: 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
Category: A1
Type: Journal Contribution
Validations: ecoom 2019
Appears in Collections:Research publications

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