Motor Control Training for the Shoulder with Smart Garments
Abstract
:1. Introduction
2. Technologies for Shoulder Posture Monitoring
3. System Design and Development
3.1. Requirements
3.2. Calculation Methods
3.3. Zishi Concept and System Overview
3.4. Garment Design and Conductive Textile Integration
3.5. Feedback Strategy
4. Evaluating Patient Attitudes towards the System
4.1. Participants
4.2. Materials
4.3. Protocol
- (1)
- Task 1: analytical shoulder flexion. The subject was asked to perform 60° of shoulder flexion with the thumb up and the elbow extended (see Figure 10a). A bar was placed in front of the patient at 60° to indicate the appropriate level of flexion. The range was determined with a goniometer, as shown in Figure 11a.
- (2)
- Task 2: functional shoulder flexion, placing a cooking pot. The subject was asked to place a cooking pot from a plate on a shelf that was located in front of him/her. The height of the shelf and the distance from the patient were standardized. The subject started with his arms alongside his body and with the elbows in 70° of flexion (determined with goniometry). The height of the shelf was adjusted accordingly. The patient had to perform 60° of shoulder flexion with extended arms, to place the pot on the shelf. Once subjects had placed the cooking pot on the shelf, they were asked to put it back on the shelf in front of them.
- (3)
- Task 3: analytical elevation in the scapular plane. The subject was asked to perform 40° of shoulder elevation in the scapular plane (30° in front of the frontal plane) with an extended elbow and with the thumb pointing upward (see Figure 10b). A bar was placed in the scapular plane, next to the patient at 40° of humerothoracic elevation to indicate the appropriate level of elevation. The range was determined with a goniometer.
- (4)
- Task 4: functional elevation in the scapular plane. The patient was asked to place a bottle of water (0.5 L) on a shelf that was located next to him in the scapular plane. The height of the shelf and the distance from the patient was standardized. At the starting position, the patient had his arms alongside his body and his elbows in 70° of flexion (measured with a goniometer). The bottle was in the hand of the tested arm side. The shelf was placed so that the patient had to perform 40° of scapular plane shoulder elevation with an extended arm to place the bottle on the shelf. Figure 11b shows a subject performing task 4.
4.4. Outcome Measures
4.4.1. Credibility and Expectancy
4.4.2. Intrinsic Motivation
4.4.3. Technology Acceptance and Usability
4.4.4. Credibility and Expectancy for Therapists
5. Results
5.1. Patients’ Evaluation
5.1.1. Measurement Results of Credibility and Expectancy
5.1.2. Measurement Results of Intrinsic Motivation
5.1.3. Technology Acceptance and Usability
5.2. Therapists’ Attitudes
6. Discussion and Future Work
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Ethical Statements
References
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Factor | Neutral | Median(IQR) | Sig | |
---|---|---|---|---|
Credibility/Expectancy (CEQ) | Credibility | 13.5 | 22.5 (3.5) | 0.011 |
Expectancy | 13.5 | 20.2 (3.55) | 0.012 | |
Intrinsic Motivation (IMI) | Interest/Enjoyment | 4 | 6.43 (0.82) | 0.012 |
Perceived competence | 4 | 5.25 (1.96) | 0.028 | |
Effort/Importance | 4 | 5.8 (1.9) | 0.025 | |
Value /Usefulness | 4 | 5.93 (1.93) | 0.012 | |
Relatedness | 4 | 5.6 (2.05) | 0.012 | |
Pressure/Tension | 4 | 2.2 (2) | 0.025 | |
Technology Acceptance (UTAUT) | Performance expectancy | 4 | 5.37 (1.75) | 0.018 |
Behavioral Intention | 4 | 5.67 (1.58) | 0.058 | |
Attitude towards technology | 4 | 5.3 (0.85) | 0.012 | |
Self-Efficacy | 4 | 5.25 (1.19) | 0.16 | |
Effort expectancy | 4 | 5.62 (1) | 0.011 | |
Facilitating conditions | 4 | 5.25 (1.44) | 0.024 | |
Usability (CSUQ) | System usefulness | 4 | 5.63 (1.53) | 0.012 |
Interface quality | 4 | 5.67 (1.33) | 0.011 |
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Wang, Q.; De Baets, L.; Timmermans, A.; Chen, W.; Giacolini, L.; Matheve, T.; Markopoulos, P. Motor Control Training for the Shoulder with Smart Garments. Sensors 2017, 17, 1687. https://doi.org/10.3390/s17071687
Wang Q, De Baets L, Timmermans A, Chen W, Giacolini L, Matheve T, Markopoulos P. Motor Control Training for the Shoulder with Smart Garments. Sensors. 2017; 17(7):1687. https://doi.org/10.3390/s17071687
Chicago/Turabian StyleWang, Qi, Liesbet De Baets, Annick Timmermans, Wei Chen, Luca Giacolini, Thomas Matheve, and Panos Markopoulos. 2017. "Motor Control Training for the Shoulder with Smart Garments" Sensors 17, no. 7: 1687. https://doi.org/10.3390/s17071687