Comparison of bilateral and unilateral upper limb training in people with stroke: A systematic review and meta-analysis

Chen, Pei Ming; Kwong, Patrick W.H.; Lai, Claudia Kam Yuk; Ng, Ssm

doi:10.1371/journal.pone.0216357

Cited by 44 publications

(41 citation statements)

References 85 publications

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“…Bimanual systems have been successfully applied in many clinical studies [9,10,11,12], and it has been shown that UE bimanual training is more beneficial to stroke patients compared to unilateral UE training [12]. Consistent with the meta-analysis of Chen et al [12] on the comparison of bilateral and unilateral upper limb training in people with stroke, our results showed improvement of the stroke participant in terms of FMA total score. Although we saw improvement in the FMA total score of the participant, this may not have necessarily yielded significant improvement in functional improvement.…”

Section: Discussionsupporting

confidence: 86%

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

Orand

Aksoy

Miyasaka

et al. 2019

Sensors

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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.

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Section: Discussionsupporting

confidence: 86%

“…Studies have shown that unaffected and affected limbs that were simultaneously moved in a bilateral training protocol were effective in the recovery of motor function in hemiplegic patients [9,10,11,12]. Activation of the nervous system and stimulation of the cerebral hemisphere by bimanual training were shown in another study [13].…”

Section: Introductionmentioning

confidence: 99%

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

Orand

Aksoy

Miyasaka

et al. 2019

Sensors

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“…Contemporary task-oriented rehabilitation interventions including the constraint-induced movement therapy (CIMT), bilateral arm training (BAT), robot-assisted therapy (RT) and mirror therapy (MT) are commonly used to address motor dysfunction in chronic stroke patients [ 10 ]. Systematic reviews and meta-analysis studies showed that these contemporary interventions were effective in improving motor function in chronic stroke patients, and should be considered in clinical application [ 11 – 14 ]. Machine learning may be a useful tool to predict motor function improvement after contemporary task-oriented interventions, which may help to identify the responders to these interventions and facilitate practical use.…”

Section: Introductionmentioning

confidence: 99%

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

Thakkar

Liao

et al. 2020

J NeuroEngineering Rehabil

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Background Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for common contemporary task-oriented interventions remains largely unexplored. This study aimed to determine the accuracy and performance of machine learning to predict clinically significant motor function improvements after contemporary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models. Methods This study was a secondary analysis of data using two common machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural network (ANN). Chronic stroke patients (N = 239) that received 30 h of task-oriented training including the constraint-induced movement therapy, bilateral arm training, robot-assisted therapy and mirror therapy were included. The Fugl-Meyer assessment scale (FMA) was the main outcome. Potential predictors include age, gender, side of lesion, time since stroke, baseline functional status, motor function and quality of life. We divided the data set into a training set and a test set and used the cross-validation procedure to construct machine learning models based on the training set. After the models were built, we used the test data set to evaluate the accuracy and prediction performance of the models. Results Three important predictors were identified, which were time since stroke, baseline functional independence measure (FIM) and baseline FMA scores. Models for predicting motor function improvements were accurate. The prediction accuracy of the KNN model was 85.42% and area under the receiver operating characteristic curve (AUC-ROC) was 0.89. The prediction accuracy of the ANN model was 81.25% and the AUC-ROC was 0.77. Conclusions Incorporating machine learning into clinical outcome prediction using three key predictors including time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are most likely to benefit from contemporary task-oriented interventions. The KNN and ANN models may be potentially useful for predicting clinically significant motor recovery in chronic stroke.

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“…FMA-UE (total 66 scores) is an assessment tool for identifying motor impairment, and it involves reflex activity (6), flexor synergy (12), extensor synergy (6), combining synergy (6), movement out of synergy (6), wrist (10), hand (14), and coordination/speed (6) on an ordinal scale from 0 to 2 (0, none; 1, partial; 2, complete). In contrast, the WMFT (total 75 scores) is a test for assessing functional performance, providing insight into joint-specific and integrative limb movements graded from 0 to 5, with 15 function-based tasks [ 39 ]. According to the study performed by Wolf et al [ 40 ], the WMFT is more sensitive than FMA-UE for assessing functional improvement in less affected stroke patients.…”

Section: Discussionmentioning

confidence: 99%

Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study

Chae¹,

Kim²,

Lee³

et al. 2020

JMIR Mhealth Uhealth

117

108

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Background Recent advancements in wearable sensor technology have shown the feasibility of remote physical therapy at home. In particular, the current COVID-19 pandemic has revealed the need and opportunity of internet-based wearable technology in future health care systems. Previous research has shown the feasibility of human activity recognition technologies for monitoring rehabilitation activities in home environments; however, few comprehensive studies ranging from development to clinical evaluation exist. Objective This study aimed to (1) develop a home-based rehabilitation (HBR) system that can recognize and record the type and frequency of rehabilitation exercises conducted by the user using a smartwatch and smartphone app equipped with a machine learning (ML) algorithm and (2) evaluate the efficacy of the home-based rehabilitation system through a prospective comparative study with chronic stroke survivors. Methods The HBR system involves an off-the-shelf smartwatch, a smartphone, and custom-developed apps. A convolutional neural network was used to train the ML algorithm for detecting home exercises. To determine the most accurate way for detecting the type of home exercise, we compared accuracy results with the data sets of personal or total data and accelerometer, gyroscope, or accelerometer combined with gyroscope data. From March 2018 to February 2019, we conducted a clinical study with two groups of stroke survivors. In total, 17 and 6 participants were enrolled for statistical analysis in the HBR group and control group, respectively. To measure clinical outcomes, we performed the Wolf Motor Function Test (WMFT), Fugl-Meyer Assessment of Upper Extremity, grip power test, Beck Depression Inventory, and range of motion (ROM) assessment of the shoulder joint at 0, 6, and 12 months, and at a follow-up assessment 6 weeks after retrieving the HBR system. Results The ML model created with personal data involving accelerometer combined with gyroscope data (5590/5601, 99.80%) was the most accurate compared with accelerometer (5496/5601, 98.13%) or gyroscope data (5381/5601, 96.07%). In the comparative study, the drop-out rates in the control and HBR groups were 40% (4/10) and 22% (5/22) at 12 weeks and 100% (10/10) and 45% (10/22) at 18 weeks, respectively. The HBR group (n=17) showed a significant improvement in the mean WMFT score (P=.02) and ROM of flexion (P=.004) and internal rotation (P=.001). The control group (n=6) showed a significant change only in shoulder internal rotation (P=.03). Conclusions This study found that a home care system using a commercial smartwatch and ML model can facilitate participation in home training and improve the functional score of the WMFT and shoulder ROM of flexion and internal rotation in the treatment of patients with chronic stroke. This strategy can possibly be a cost-effective tool for the home care treatment of stroke survivors in the future. Trial Registration Clinical Research Information Service KCT0004818; https://tinyurl.com/y92w978t

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Comparison of bilateral and unilateral upper limb training in people with stroke: A systematic review and meta-analysis

Cited by 44 publications

References 85 publications

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

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

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study

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