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Open AccessArticle

Bilateral Tactile Feedback-Enabled Training for Stroke Survivors Using Microsoft KinectTM

1
Department of Intelligent Systems and Digital Design, School of Information Technology, Halmstad University, Spetsvinkelgatan 29, 30250 Halmstad, Sweden
2
Department of Rehabilitation, Fujita Health University Nanakuri Memorial Hospital, 424-1 Oodori-cho, Tsu, Mie 514-1296, Japan
3
Schools of Mechatronics Systems Engineering and Engineering Science, Simon Fraser University, 250-13450 102 Avenue, Surrey, BC V3T 0A3, Canada
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3474; https://doi.org/10.3390/s19163474
Received: 13 June 2019 / Revised: 1 August 2019 / Accepted: 5 August 2019 / Published: 8 August 2019
(This article belongs to the Special Issue Sensor Fusion in Assistive and Rehabilitation Robotics)
Rehabilitation and mobility training of post-stroke patients is crucial for their functional recovery. While traditional methods can still help patients, new rehabilitation and mobility training methods are necessary to facilitate better recovery at lower costs. In this work, our objective was to design and develop a rehabilitation training system targeting the functional recovery of post-stroke users with high efficiency. To accomplish this goal, we applied a bilateral training method, which proved to be effective in enhancing motor recovery using tactile feedback for the training. One participant with hemiparesis underwent six weeks of training. Two protocols, “contralateral arm matching” and “both arms moving together”, were carried out by the participant. Each of the protocols consisted of “shoulder abduction” and “shoulder flexion” at angles close to 30 and 60 degrees. The participant carried out 15 repetitions at each angle for each task. For example, in the “contralateral arm matching” protocol, the unaffected arm of the participant was set to an angle close to 30 degrees. He was then requested to keep the unaffected arm at the specified angle while trying to match the position with the affected arm. Whenever the two arms matched, a vibration was given on both brachialis muscles. For the “both arms moving together” protocol, the two arms were first set approximately to an angle of either 30 or 60 degrees. The participant was asked to return both arms to a relaxed position before moving both arms back to the remembered specified angle. The arm that was slower in moving to the specified angle received a vibration. We performed clinical assessments before, midway through, and after the training period using a Fugl-Meyer assessment (FMA), a Wolf motor function test (WMFT), and a proprioceptive assessment. For the assessments, two ipsilateral and contralateral arm matching tasks, each consisting of three movements (shoulder abduction, shoulder flexion, and elbow flexion), were used. Movements were performed at two angles, 30 and 60 degrees. For both tasks, the same procedure was used. For example, in the case of the ipsilateral arm matching task, an experimenter positioned the affected arm of the participant at 30 degrees of shoulder abduction. The participant was requested to keep the arm in that position for ~5 s before returning to a relaxed initial position. Then, after another ~5-s delay, the participant moved the affected arm back to the remembered position. An experimenter measured this shoulder abduction angle manually using a goniometer. The same procedure was repeated for the 60 degree angle and for the other two movements. We applied a low-cost Kinect to extract the participant’s body joint position data. Tactile feedback was given based on the arm position detected by the Kinect sensor. By using a Kinect sensor, we demonstrated the feasibility of the system for the training of a post-stroke user. The proposed system can further be employed for self-training of patients at home. The results of the FMA, WMFT, and goniometer angle measurements showed improvements in several tasks, suggesting a positive effect of the training system and its feasibility for further application for stroke survivors’ rehabilitation. View Full-Text
Keywords: Kinect; stroke rehabilitation; bilateral training; tactile feedback Kinect; stroke rehabilitation; bilateral training; tactile feedback
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MDPI and ACS Style

Orand, A.; Erdal Aksoy, E.; Miyasaka, H.; Weeks Levy, C.; Zhang, X.; Menon, C. Bilateral Tactile Feedback-Enabled Training for Stroke Survivors Using Microsoft KinectTM. Sensors 2019, 19, 3474.

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