Quantitative Evaluation System of Upper Limb Motor Function of Stroke Patients Based on Desktop Rehabilitation Robot

Three-Dimensional Magnetic Rehabilitation, Robot-Enhanced Hand-Motor Recovery after Subacute Stroke: A Randomized Controlled Trial

We developed an end-effector-type rehabilitation robot that can uses electro- and permanent magnets to generate a three-way magnetic field to assist hand movements and perform rehabilitation therapy. This study aimed to investigate the therapeutic effect of a rehabilitation program using a three-dimensional (3D) magnetic force-based hand rehabilitation robot on the motor function recovery of the paralyzed hands of patients with stroke. This was a double-blind randomized controlled trial in which 36 patients with subacute stroke were assigned to intervention and control groups of 18 patients each. The intervention group received 30 min of rehabilitation therapy per day for a month using a 3D magnetic force-driven hand rehabilitation robot, whereas the control group received 30 min of conventional occupational therapy to restore upper-limb function. The patients underwent three behavioral assessments at three time points: before starting treatment (T0), after 1 month of treatment (T1), and at the follow-up 1-month after treatment completion (T2). The primary outcome measure was the Wolf Motor Function Test (WMFT), and secondary outcome measures included the Fugl-Meyer Assessment of the Upper Limb (FMA_U), Modified Barthel Index (MBI), and European Quality of Life Five Dimensions (EQ-5D) questionnaire. No participant safety issues were reported during the intervention. Analysis using repeated measures analysis of variance showed significant interaction effects between time and group for both the WMFT score (p = 0.012) and time (p = 0.010). In post hoc analysis, the WMFT scores and time improved significantly more in the patients who received robotic rehabilitation at T1 than in the controls (p = 0.018 and p = 0.012). At T2, we also consistently found improvements in both the WMFT scores and times for the intervention group that were superior to those in the control group (p = 0.024 and p = 0.018, respectively). Similar results were observed for FMA_U, MBI, and EQ-5D. Rehabilitation using the 3D hand-rehabilitation robot effectively restored hand function in the patients with subacute stroke, contributing to improvement in daily independence and quality of life.

Data-Driven Quantitation of Movement Abnormality after Stroke

Stroke commonly affects the ability of the upper extremities (UEs) to move normally. In clinical settings, identifying and measuring movement abnormality is challenging due to the imprecision and impracticality of available assessments. These challenges interfere with therapeutic tracking, communication, and treatment. We thus sought to develop an approach that blends precision and pragmatism, combining high-dimensional motion capture with out-of-distribution (OOD) detection. We used an array of wearable inertial measurement units to capture upper body motion in healthy and chronic stroke subjects performing a semi-structured, unconstrained 3D tabletop task. After data were labeled by human coders, we trained two deep learning models exclusively on healthy subject data to classify elemental movements (functional primitives). We tested these healthy subject-trained models on previously unseen healthy and stroke motion data. We found that model confidence, indexed by prediction probabilities, was generally high for healthy test data but significantly dropped when encountering OOD stroke data. Prediction probabilities worsened with more severe motor impairment categories and were directly correlated with individual impairment scores. Data inputs from the paretic UE, rather than trunk, most strongly influenced model confidence. We demonstrate for the first time that using OOD detection with high-dimensional motion data can reveal clinically meaningful movement abnormality in subjects with chronic stroke.

Clinical Study of a Wearable Remote Rehabilitation Training System for Patients With Stroke: Randomized Controlled Pilot Trial

BACKGROUND

In contrast to the large and increasing number of patients with stroke, clinical rehabilitation resources cannot meet their rehabilitation needs. Especially for those discharged, ways to carry out effective rehabilitation training without the supervision of physicians and receive guidance from physicians remain urgent problems to be solved in clinical rehabilitation and have become a research hot spot at home and abroad. At present, there are many studies on home rehabilitation training based on wearable devices, Kinect, among others, but these have disadvantages (eg, complex systems, high price, and unsatisfactory rehabilitation effects).

OBJECTIVE

This study aims to design a remote intelligent rehabilitation training system based on wearable devices and human-computer interaction training tasks, and to evaluate the effectiveness and safety of the remote rehabilitation training system for nonphysician-supervised motor rehabilitation training of patients with stroke through a clinical trial study.

METHODS

A total of 120 inpatients with stroke having limb motor dysfunction were enrolled via a randomized, parallel-controlled method in the rehabilitation institutions, and a 3-week clinical trial was conducted in the rehabilitation hall with 60 patients in the experimental group and 60 in the control group. The patients in the experimental group used the remote rehabilitation training system for rehabilitation training and routine clinical physical therapy (PT) training and received routine drug treatment every day. The patients in the control group received routine clinical occupational therapy (OT) training and routine clinical PT training and routine drug treatment every day. At the beginning of the training (baseline) and after 3 weeks, the Fugl-Meyer Motor Function Rating scale was scored by rehabilitation physicians, and the results were compared and analyzed.

RESULTS

Statistics were performed using SAS software (version 9.4). The total mean Fugl-Meyer score improved by 11.98 (SD 8.46; 95% CI 9.69-14.27) in the control group and 17.56 (SD 11.65; 95% CI 14.37-20.74) in the experimental group, and the difference between the 2 groups was statistically significant (P=.005). Among them, the mean Fugl-Meyer upper extremity score improved by 7.45 (SD 7.24; 95% CI 5.50-9.41) in the control group and 11.28 (SD 8.59; 95% CI 8.93-13.62) in the experimental group, and the difference between the 2 groups was statistically significant (P=.01). The mean Fugl-Meyer lower extremity score improved by 4.53 (SD 4.42; 95% CI 3.33-5.72) in the control group and 6.28 (SD 5.28; 95% CI 4.84-7.72) in the experimental group, and there was no significant difference between the 2 groups (P=.06). The test results showed that the experimental group was better than the control group, and that the patients' motor ability was improved.

CONCLUSIONS

The remote rehabilitation training system designed based on wearable devices and human-computer interaction training tasks can replace routine clinical OT training. In the future, through medical device registration certification, the system will be used without the participation of physicians or therapists, such as in rehabilitation training halls, and in remote environments, such as communities and homes.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR2200061310; https://tinyurl.com/34ka2725.

Spring damping based control for a novel lower limb rehabilitation robot with active flexible training planning

BACKGROUND

During neurological rehabilitation training for patients with lower limb dysfunction, active rehabilitation training based on interactive force recognition can effectively improve participation and efficiency in rehabilitation training.

OBJECTIVE

This study proposes an active training strategy for lower-limb rehabilitation robots based on a spring damping model.

METHODS

The active training strategy included a kinetic model of the human-machine system, calculated and verified using a pull-pressure force sensor We used a dynamic model of the human-machine system and tensile force sensors to identify the human-machine interaction forces exerted by the patient Finally, the spring damping model is used to convert the active interaction force into the offset angle of each joint, obtaining the active interaction force followed by the active movement of the lower limbsRESULTS:The experimental results showed that the rehabilitation robot could follow the active interaction force of the subject to provide assistance, thus generating the following movement and effectively helping patients improve joint mobility.

CONCLUSION

The active flexibility training control strategy based on the virtual spring damping model proposed in this study is feasible, and motion is stable for patients with lower limb dysfunction after stroke Finally, the proposed active training method can be implemented in future work in other rehabilitation equipment and combined virtual reality technology to improve rehabilitation training experience and increase patient participation.

Wearable Intelligent Machine Learning Rehabilitation Assessment for Stroke Patients Compared with Clinician Assessment

In order to solve the shortcomings of the current clinical scale assessment for stroke patients, such as excessive time consumption, strong subjectivity, and coarse grading, this study designed an intelligent rehabilitation assessment system based on wearable devices and a machine learning algorithm and explored the effectiveness of the system in assessing patients’ rehabilitation outcomes. The accuracy and effectiveness of the intelligent rehabilitation assessment system were verified by comparing the consistency and time between the designed intelligent rehabilitation assessment system scores and the clinical Fugl−Meyer assessment (FMA) scores. A total of 120 stroke patients from two hospitals participated as volunteers in the trial study, and statistical analyses of the two assessment methods were performed. The results showed that the R2 of the total score regression analysis for both methods was 0.9667, 95% CI 0.92−0.98, p < 0.001, and the mean of the deviation was 0.30, 95% CI 0.57−1.17. The percentages of deviations/relative deviations falling within the mean ± 1.96 SD of deviations/relative deviations were 92.50% and 95.83%, respectively. The mean time for system assessment was 35.00% less than that for clinician assessment, p < 0.05. Therefore, wearable intelligent machine learning rehabilitation assessment has a strong and significant correlation with clinician assessment, and the time spent is significantly reduced, which provides an accurate, objective, and effective solution for clinical rehabilitation assessment and remote rehabilitation without the presence of physicians.

Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback

Motor function evaluation is a significant part of post-stroke rehabilitation protocols, and the evaluation of wrist motor function helps provide patients with individualized rehabilitation training programs. However, traditional assessment is coarsely graded, lacks quantitative analysis, and relies heavily on clinical experience. In order to objectively quantify wrist motor dysfunction in stroke patients, a novel quantitative evaluation system based on force feedback and machine learning algorithm was proposed. Sensors embedded in the force-feedback robot record the kinematic and movement data of the subject, and the rehabilitation doctor used an evaluation scale to score the wrist function of the subject. The quantitative evaluation models of wrist motion function based on random forest (RF), support vector machine regression (SVR), k-nearest neighbor (KNN), and back propagation neural network (BPNN) were established, respectively. To verify the effectiveness of the proposed quantitative evaluation system, 25 stroke patients and 10 healthy volunteers were recruited in this study. Experimental results show that the evaluation accuracy of the four models is all above 88%. The accuracy of BPNN model is 94.26%, and the Pearson correlation coefficient between model prediction and clinician scores is 0.964, indicating that the BPNN model can accurately evaluate the wrist motor function for stroke patients. In addition, there was a significant correlation between the prediction score of the quantitative assessment system and the physician scale score (p < 0.05). The proposed system enables quantitative and refined assessment of wrist motor function in stroke patients and has the feasibility of helping rehabilitation physicians in evaluating patients’ motor function clinically.

Effects of Early Acupuncture Combined with Rehabilitation Training on Limb Function and Nerve Injury Rehabilitation in Elderly Patients with Stroke: Based on a Retrospective Cohort Study

Objective

A case-control study was conducted to explore the effect of acupuncture combined with rehabilitation training on limb function and nerve injury rehabilitation in elderly patients with stroke.

Methods

A total of 72 elderly patients with stroke treated from March 2019 to June 2021 in our hospital were enrolled as the object of study. The clinical data were collected and divided into two groups according to their different treatment methods. The patients cured with routine treatment combined with rehabilitation training were taken as the control group and the patients cured with acupuncture combined with rehabilitation training as the study group. The clinical efficacy was recorded, and the cognition and activities of daily living were evaluated by Terrell Cognitive Assessment scale, limb motor function score, and activities of daily living scale. The National Institutes of Health Stroke Scale (NIHSS) and Glasgow Coma Scale (GCS) were employed to compare the neurological function before and after treatment. Glasgow Outcome Scale (GOS) and Disability Rating Scale (DRS) were adopted to evaluate the functional prognosis. The simplified Fugl-Meyer assessment of motor recovery score was employed to evaluate the limb function of the patients. The Wolf Motor Function Test (WMFT) score was adopted to evaluate the functional rehabilitation effect of the patients. Enzyme-linked immunosorbent assay (ELISA) was adopted to determine the serum neurological function indexes such as nerve growth factor, Smur100B protein, and glial fibrillary acidic protein. The cerebral blood flow (CBF), peak time, average transit time, and cerebral blood volume were measured by CT perfusion imaging, and the incidence of side effects during treatment was recorded.

Results

Regarding the recovery of cognitive function and daily function after treatment, after treatment, the MoCA and ADL scores were increased, and the comparison indicated that the MoCA and ADL scores of the study group were remarkably higher compared to the control group (P < 0.05). With regard to the FMA-UE scores after treatment, the Fugl-Meyer scores were gradually increased, and the Fugl-Meyer scores in the study group were remarkably higher compared to the control group (P < 0.05) in the next two months. After 2 weeks, 4 weeks, 6 weeks, and 6 weeks of treatment, the WMFT scores gradually increased, and the WMFT score of the study group was remarkably higher compared to the control group. After treatment, the levels of nerve growth factor and S-100B protein were decreased, and the level of glial fibrillary acidic protein was increased. Comparison between the two groups, it indicated the improvement degree of each neurological function index in the study group was remarkably better (P < 0.05). With regard to cerebral hemodynamic indexes after treatment, 1 week after treatment, the CBF and average transit time of the observation group were remarkably higher compared to the control group, and the levels of cerebral blood volume and peak time were remarkably lower compared to the control group (P < 0.05). After 4 weeks of treatment, the cerebral hemodynamic indexes of the observation group did not change remarkably, and they were all lower than 1 week after the treatment. In the terms of side effects, 1 case of limb dysfunction, 1 case of swallowing dysfunction, 1 case of electrolyte disturbance, and none of infection in the study group, the incidence of adverse reactions was 8.33%. In the control group, there were 3 cases of limb dysfunction, 2 cases of swallowing dysfunction, 2 cases of electrolyte disturbance, and 3 cases of infection, and the incidence of adverse reactions was 27.78%. Compared between groups, the incidence of adverse reactions in the study group was lower (P < 0.05).

Conclusion

Early use of acupuncture combined with rehabilitation training has a remarkable therapeutic effect on elderly stroke patients. It can remarkably promote the recovery of the patient's condition, remarkably enhance their neurological function, cognitive function, motor function, and daily life function, and effectively strengthen the patient's prognosis score. It has important clinical application value to reduce the incidence of adverse reactions.