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      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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          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.


          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.


          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.


          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).


          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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          Most cited references44

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          Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

          Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.
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            Assessing Wolf motor function test as outcome measure for research in patients after stroke.

            The Wolf Motor Function Test (WMFT) is a new time-based method to evaluate upper extremity performance while providing insight into joint-specific and total limb movements. This study addresses selected psychometric attributes of the WMFT applied to a chronic stroke population. Nineteen individuals after stroke and with intact cognition and sitting balance were age- and sex-matched with 19 individuals without impairment. Subjects performed the WMFT and the upper extremity portion of the Fugl-Meyer Motor Assessment (FMA) on 2 occasions (12 to 16 days apart), with scoring performed independently by 2 random raters. The WMFT and FMA demonstrated agreement (P 0.05) from the dominant and nondominant extremities of individuals without impairment. The WMFT and FMA scores were related (P<0.02) for the more affected extremity in individuals after stroke. The interrater reliability, construct validity, and criterion validity of the WMFT, as used in these subject samples, are supported.
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              Risk and cumulative risk of stroke recurrence: a systematic review and meta-analysis.

              Estimates of risk of stroke recurrence are widely variable and focused on the short- term. A systematic review and meta-analysis was conducted to estimate the pooled cumulative risk of stroke recurrence. Studies reporting cumulative risk of recurrence after first-ever stroke were identified using electronic databases and by manually searching relevant journals and conference abstracts. Overall cumulative risks of stroke recurrence at 30 days and 1, 5, and 10 years after first stroke were calculated, and analyses for heterogeneity were conducted. A Weibull model was fitted to the risk of stroke recurrence of the individual studies and pooled estimates were calculated with 95% CI. Sixteen studies were identified, of which 13 studies reported cumulative risk of stroke recurrence in 9115 survivors. The pooled cumulative risk was 3.1% (95% CI, 1.7-4.4) at 30 days, 11.1% (95% CI, 9.0-13.3) at 1 year, 26.4% (95% CI, 20.1-32.8) at 5 years, and 39.2% (95% CI, 27.2-51.2) at 10 years after initial stroke. Substantial heterogeneity was found at all time points. This study also demonstrates a temporal reduction in 5-year risk of stroke recurrence from 32% to 16.2% across the studies. The cumulative risk of recurrence varies greatly up to 10 years. This may be explained by differences in case mix and changes in secondary prevention over time However, methodological differences are likely to play an important role and consensus on definitions would improve future comparability of estimates and characterization of groups of stroke survivors at increased risk of recurrence.

                Author and article information

                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                July 2020
                9 July 2020
                : 8
                : 7
                [1 ] Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea
                [2 ] Major of Sports Health Rehabilitation Cheongju University Cheongju Republic of Korea
                [3 ] Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea
                Author notes
                Corresponding Author: Hyung-Soon Park hyungspark@ 123456kaist.ac.kr
                ©Sang Hoon Chae, Yushin Kim, Kyoung-Soub Lee, Hyung-Soon Park. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 09.07.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                Original Paper
                Original Paper

                home-based rehabilitation,artificial intelligence,machine learning,wearable device,smartwatch,chronic stroke


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