Objective To systematically evaluate the effect of intermittent theta burst stimulation(iTBS)on lower extremity motor function and balance function in patients with stroke. Methods A systematic literature search was conducted in the Cochrane Library,Embase,PubMed,Web of Science,CNKI,SinoMed,Wanfang data and VIP from inception to February 19,2025.Randomized controlled trials com-paring iTBS with conventional rehabilitation or sham iTBS in patients with post-stroke lower extremity motor and balance dysfunction were included.The methodological quality of the included studies was assessed using the Cochrane Risk of Bias Tool.Meta-analysis was performed using RevMan 5.4 and Stata 14.0. Results A total of 16 articles involving 647 patients were included.Meta-analysis showed that iTBS improved the Fugl-Meyer Assessment-Lower Extremities score(MD=2.58,95％CI 1.61 to 3.55,P<0.001),Berg Balance Scale score(MD=4.11,95％CI 2.43 to 5.79,P<0.001),Barthel Index score(MD=4.95,95％CI 0.97 to 8.92,P=0.010)and motor-evoked potential(MEP)latency(MD=-1.42,95％CI-2.54 to-0.30,P=0.010).Subgroup analyses suggested that at subacute stage,cerebellar iTBS,more than ten treatment sessions,and 1 200 pulses per day may be more effective. Conclusion iTBS may improve lower extremity motor function and activities of daily living in patients with stroke,and shorten MEP latency,and it may also confer potential benefits in improving balance function in these patients.

Motor function Evaluation is an indispensable part of rehabilitation treatment for stroke patients. As a rule, this process is manually performed by the experience of therapists which is subjective. Recently, research on rehabilitation assessment based on data provided by wearable sensors has provided convenient and practical solutions. In order to objectively evaluate the function of hemiplegic upper-extremities (HUEs) patients, we propose 2-dimension convolution neural network (2DCNN) for the Brunnstrom Stage (BS) classification. As a contrast, an attention Bidirectional Long Short-term Memory model (Attention-BiLSTM) and traditional machine learning approaches Support Vector Machine (SVM), Random Forest (RF) and multi-layer perceptron (MLP) were also adopted, which uses the surface electromyography (sEMG) and inertial measurement unit (IMU) data provided by a single wearable sensor on the upper extremity of stroke patients. The 2DCNN can achieve classification accuracy of 93.46% in segments for 3 levels (BS IV, BS V and healthy status), which is better than Attention-BiLSTM and traditional machine learning approaches (p < 0.05). The proposed model can fastly and efficiently assess stroke patients-functional level at a short time interval (300 ms), which can assist therapists with stroke rehabilitation.

Abstract Background Impairments in upper limb motor function and cognitive ability are major health problems experienced by stroke patients, necessitating the development of novel and effective treatment options in stroke care. The aim of this study is to examine the effects of robot-assisted therapy on improving upper limb and cognitive functions in stroke patients. Methods This will be a single-blinded, 2-arm, parallel design, randomized controlled trial which will include a sample size of 86 acute and subacute stroke patients to be recruited from a single clinical hospital in Shanghai, China. Upon qualifying the study eligibility, participants will be randomly assigned to receive either robot-assisted therapy or conventional therapy with both interventions being conducted over a 6-week period in a clinical rehabilitation setting. In addition to comprehensive rehabilitation, the robot-assisted therapy group will receive a 30-min Armguider robot-assisted therapy intervention 5 days a week. Primary efficacy outcomes will include Fugl-Meyer Assessment for Upper Extremity (FMA-UE) and Mini-Mental Status Examination (MMSE). Other secondary outcomes will include Trail Making Test (TMT), Auditory Verbal Learning Test (AVLT), Digit Symbol Substitution Test (DSST), and Rey–Osterrieth Complex Figure Test (ROCFT). All trial outcomes will be assessed at baseline and at 6-week follow-up. Intention-to-treat analyses will be performed to examine changes from baseline in the outcomes. Adverse events will be monitored throughout the trial period. Discussion This will be the first randomized controlled trial aimed at examining the effects of robot-assisted therapy on upper limb and cognitive functions in acute and subacute stroke patients. Findings from the study will contribute to our understanding of using a novel robotic rehabilitation approach to stroke care and rehabilitation. Trial registration Chinese Clinical Trial Registry ChiCTR2100050856 . Registered on 5 September 2021.

Motor function evaluation plays an important role in post-stroke rehabilitation. However, the traditional evaluation is subjective and laborious, which may bring a heavy burden to both physicians and stroke survivors. Therefore, an automatic and objective rehabilitation evaluation is needed to minimize the burden of physician, so as to achieve a simplified and objective evaluation process. The main purpose of this study is to investigate the minimum number of tasks for upper-extremity actions in objective assessment of stroke survivors with a Brunnstrom stage (BS) based on wearable sensing device, which can achieve a satisfactory result to reduce the burden of stroke survivors. In this study, we employed 20 stroke survivors and 7 healthy participants, performing three types of daily living activities (drinking, teeth brushing, face washing). The acceleration, angular velocity and surface Electromyography signals on five parts of the forearm were simultaneously acquired. Then, we compared the effects of each action combination under multiple classifiers. The results show that the use of a single action can achieve competitive results compared with multiple action combination classifications, and the use of K nearest neighbor (KNN) algorithm for the average recognition accuracy of face washing action shows better performance, with the highest accuracy reaching 85.65±6.21% (mean ± standard error), 23 of the 27 subjects were accurately classified. These findings indicate that the predominant qualitative assessment after stroke can be supplemented by corresponding quantitative solutions, and that stroke rehabilitation can be automated with less professional therapist involvement.

目的 研究可同时用于健康人和脑卒中患者活动分类的传感模块最优位置和数量.方法 23名受试者(14名健康人,9名脑卒中患者)完成步行、刷牙、洗脸和喝水4个动作,每人身上佩戴12个传感模块,各个传感模块集成了加速度、陀螺仪和肌电传感器.采用最大相关-最小冗余算法(mRMR)进行特征选择,随后采用核函数为径向基函数的支持向量机,并利用留一交叉验证法进行活动分类评估.基于活动分类的结果,利用前向逐步选择算法得到每种数量的传感模块(共12种)放置的最佳位置,并得到12个活动识别准确率和其中的最高准确率.计算12个准确率的平均值,准确率超过平均准确率且与最高准确率无显著性差异的最少传感模块组合作为本研究的最优传感模块组合.结果 最优的传感模块组合是采用位于上肢尺侧腕伸肌和下肢股二头肌长头的两个传感模块,其活动分类准确率能达到97.9％±0.94％.结论 采用最优的传感模块组合达到了较高的准确率,其在活动识别方面具有很大的应用潜力.

Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris.