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Please use this identifier to cite or link to this item: 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
Citation: 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
URI: http://hdl.handle.net/1942/24041
Link to publication: http://www.mdpi.com/1424-8220/17/7/1687
DOI: 10.3390/s17071687
ISI #: 000407517600226
ISSN: 1424-8220
Category: A1
Type: Journal Contribution
Validation: ecoom, 2018
Appears in Collections: Research publications

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