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http://hdl.handle.net/1942/24041
Title: | Motor Control Training for the Shoulder with Smart Garments | Authors: | Wang, Qi DE BAETS, Liesbet TIMMERMANS, Annick Chen, Wei Giacolini, Luca MATHEVE, Thomas Markopoulos, Panos |
Issue Date: | 2017 | Source: | Sensors, 17(7), (Art N° E1687) | Abstract: | Wearable technologies for posture monitoring and posture correction are emerging as a way to support and enhance physical therapy treatment, e.g., for motor control training in neurological disorders or for treating musculoskeletal disorders, such as shoulder, neck, or lower back pain. Among the various technological options for posture monitoring, wearable systems offer potential advantages regarding mobility, use in different contexts and sustained tracking in daily life. We describe the design of a smart garment named Zishi to monitor compensatory movements and evaluate its applicability for shoulder motor control training in a clinical setting. Five physiotherapists and eight patients with musculoskeletal shoulder pain participated in the study. The attitudes of patients and therapists towards the system were measured using standardized survey instruments. The results indicate that patients and their therapists consider Zishi a credible aid for rehabilitation and patients expect it will help towards their recovery. The system was perceived as highly usable and patients were motivated to train with the system. Future research efforts on the improvement of the customization of feedback location and modality, and on the evaluation of Zishi as support for motor learning in shoulder patients, should be made. | Notes: | Wang, Q (reprint author), Eindhoven Univ Technol, Dept Ind Design, NL-5612 AZ Eindhoven, Netherlands. q.wang@tue.nl; liesbet.debaets@uhasselt.be; annick.timmermans@uhasselt.be; w_chen@fudan.edu.cn; luca.giacolini.16@ucl.ac.uk; thomas.matheve@uhasselt.be; p.markopoulos@tue.nl | Keywords: | compensatory movement; posture monitoring; rehabilitation; shoulder training; wearable system | Document URI: | http://hdl.handle.net/1942/24041 | Link to publication/dataset: | http://www.mdpi.com/1424-8220/17/7/1687 | ISSN: | 1424-8220 | e-ISSN: | 1424-8220 | DOI: | 10.3390/s17071687 | ISI #: | 000407517600226 | Rights: | © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2018 |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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sensors-17-01687-v2.pdf | Published version | 6.15 MB | Adobe PDF | View/Open |
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