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BACKGROUND: The loss of gait automaticity is a key cause of motor deficits in Parkinson's disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation. METHODS: Thirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests. RESULTS: The turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen's d = 0.933). CONCLUSION: The turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment. Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657.

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This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabilitation progress by distinguishing gait characteristics under different walking modes. Equipped with accelerometers and six-axis force sensors, the device monitors body symmetry and upper limb strength during rehabilitation. Data were collected from normal and abnormal walking groups. A knee joint limiter was applied to subjects to simulate different levels of movement disorders. Features were extracted from the collected data and analyzed using a CNN. The overall performance was scored with Random Forest Model weights. Significant differences in average acceleration values between the moderately abnormal (MA) and severely abnormal (SA) groups (without vehicle assistance) were observed (p < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups (p > 0.05). Force sensor data showed good concentration in the normal walking group and more scatter in the SA-V group. The CNN and Random Forest Model accurately recognized gait conditions, achieving average accuracies of 88.4% and 92.3%, respectively, proving that the method mentioned above provides more accurate gait evaluations for patients with movement disorders.

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OBJECTIVE: To avoid deviation caused by the traditional scale method, the present study explored the accuracy, advantages, and disadvantages of different objective detection methods in evaluating lower extremity motor function in elderly individuals. METHODS: Studies on lower extremity motor function assessment in elderly individuals published in the PubMed, Web of Science, Cochrane Library and EMBASE databases in the past five years were searched. The methodological quality of the included trials was assessed using RevMan 5.4.1 and Stata, followed by statistical analyses. RESULTS: In total, 19 randomized controlled trials with a total of 2626 participants, were included. The results of the meta-analysis showed that inertial measurement units (IMUs), motion sensors, 3D motion capture systems, and observational gait analysis had statistical significance in evaluating the changes in step velocity and step length of lower extremity movement in elderly individuals (P < 0.00001), which can be used as a standardized basis for the assessment of motor function in elderly individuals. Subgroup analysis showed that there was significant heterogeneity in the assessment of step velocity [SMD=-0.98, 95%CI(-1.23, -0.72), I2 = 91.3%, P < 0.00001] and step length [SMD=-1.40, 95%CI(-1.77, -1.02), I2 = 86.4%, P < 0.00001] in elderly individuals. However, the sensors (I2 = 9%, I2 = 0%) and 3D motion capture systems (I2 = 0%) showed low heterogeneity in terms of step velocity and step length. The sensitivity analysis and publication bias test demonstrated that the results were stable and reliable. CONCLUSION: observational gait analysis, motion sensors, 3D motion capture systems, and IMUs, as evaluation means, play a certain role in evaluating the characteristic parameters of step velocity and step length in lower extremity motor function of elderly individuals, which has good accuracy and clinical value in preventing motor injury. However, the high heterogeneity of observational gait analysis and IMUs suggested that different evaluation methods use different calculation formulas and indicators, resulting in the failure to obtain standardized indicators in clinical applications. Thus, multimodal quantitative evaluation should be integrated.

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This study aimed to propose a portable and intelligent rehabilitation evaluation system for digital stroke-patient rehabilitation assessment. Specifically, the study designed and developed a fusion device capable of emitting red, green, and infrared lights simultaneously for photoplethysmography (PPG) acquisition. Leveraging the different penetration depths and tissue reflection characteristics of these light wavelengths, the device can provide richer and more comprehensive physiological information. Furthermore, a Multi-Channel Convolutional Neural Network-Long Short-Term Memory-Attention (MCNN-LSTM-Attention) evaluation model was developed. This model, constructed based on multiple convolutional channels, facilitates the feature extraction and fusion of collected multi-modality data. Additionally, it incorporated an attention mechanism module capable of dynamically adjusting the importance weights of input information, thereby enhancing the accuracy of rehabilitation assessment. To validate the effectiveness of the proposed system, sixteen volunteers were recruited for clinical data collection and validation, comprising eight stroke patients and eight healthy subjects. Experimental results demonstrated the system's promising performance metrics (accuracy: 0.9125, precision: 0.8980, recall: 0.8970, F1 score: 0.8949, and loss function: 0.1261). This rehabilitation evaluation system holds the potential for stroke diagnosis and identification, laying a solid foundation for wearable-based stroke risk assessment and stroke rehabilitation assistance.

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The clinical rehabilitation assessment methods for hemiplegic upper limb motor function are often subjective, time-consuming, and non-uniform. This study proposes an automatic rehabilitation assessment method for upper limb motor function based on posture and distributed force measurements. Azure Kinect combined with MediaPipe was used to detect upper limb and hand movements, and the array distributed flexible thin film pressure sensor was employed to measure the distributed force of hand. This allowed for the automated measurement of 30 items within the Fugl-Meyer scale. Feature information was extracted separately from the affected and healthy sides, the feature ratios or deviation were then fed into a single/multiple fuzzy logic assessment model to determine the assessment score of each item. Finally, the total score of the hemiplegic upper limb motor function assessment was derived. Experiments were performed to evaluate the motor function of the subjects' upper extremities. Bland-Altman plots of physician and system scores showed good agreement. The results of the automated assessment system were highly correlated with the clinical Fugl-Meyer total score (r = 0.99, p < 0.001). The experimental results state that this system can automatically assess the motor function of the affected upper limb by measuring the posture and force distribution.

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Stroke represents a medical emergency and can lead to the development of movement disorders such as abnormal muscle tone, limited range of motion, or abnormalities in coordination and balance. In order to help stroke patients recover as soon as possible, rehabilitation training methods employ various movement modes such as ordinary movements and joint reactions to induce active reactions in the limbs and gradually restore normal functions. Rehabilitation effect evaluation can help physicians understand the rehabilitation needs of different patients, determine effective treatment methods and strategies, and improve treatment efficiency. In order to achieve real-time and accuracy of action detection, this article uses Mediapipe's action detection algorithm and proposes a model based on MPL-CNN. Mediapipe can be used to identify key point features of the patient's upper limbs and simultaneously identify key point features of the hand. In order to detect the effect of rehabilitation training for upper limb movement disorders, LSTM and CNN are combined to form a new LSTM-CNN model, which is used to identify the action features of upper limb rehabilitation training extracted by Medipipe. The MPL-CNN model can effectively identify the accuracy of rehabilitation movements during upper limb rehabilitation training for stroke patients. In order to ensure the scientific validity and unified standards of rehabilitation training movements, this article employs the postures in the Fugl-Meyer Upper Limb Rehabilitation Training Functional Assessment Form (FMA) and establishes an FMA upper limb rehabilitation data set for experimental verification. Experimental results show that in each stage of the Fugl-Meyer upper limb rehabilitation training evaluation effect detection, the MPL-CNN-based method's recognition accuracy of upper limb rehabilitation training actions reached 95%. At the same time, the average accuracy rate of various upper limb rehabilitation training actions reaches 97.54%. This shows that the model is highly robust across different action categories and proves that the MPL-CNN model is an effective and feasible solution. This method based on MPL-CNN can provide a high-precision detection method for the evaluation of rehabilitation effects of upper limb movement disorders after stroke, helping clinicians in evaluating the patient's rehabilitation progress and adjusting the rehabilitation plan based on the evaluation results. This will help improve the personalization and precision of rehabilitation treatment and promote patient recovery.

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Although the 6-Minute Walk Test (6MWT) is among the recommended clinical tools to assess gait impairments in individuals with Parkinson's disease (PD), its standard clinical outcome consists only of the distance walked in 6 min. Integrating a single Inertial Measurement Unit (IMU) could provide additional quantitative and objective information about gait quality complementing standard clinical outcome. This study aims to evaluate the test-retest reliability, validity and discriminant ability of gait parameters obtained by a single IMU during the 6MWT in subjects with mild PD. Twenty-two people with mild PD and ten healthy persons performed the 6MWT wearing an IMU placed on the lower trunk. Features belonging to rhythm and pace, variability, regularity, jerkiness, intensity, dynamic instability and symmetry domains were computed. Test-retest reliability was evaluated through the Intraclass Correlation Coefficient (ICC), while concurrent validity was determined by Spearman's coefficient. Mann-Whitney U test and the Area Under the receiver operating characteristic Curve (AUC) were then applied to assess the discriminant ability of reliable and valid parameters. Results showed an overall high reliability (ICC ≥ 0.75) and multiple significant correlations with clinical scales in all domains. Several features exhibited significant alterations compared to healthy controls. Our findings suggested that the 6MWT instrumented with a single IMU can provide reliable and valid information about gait features in individuals with PD. This offers objective details about gait quality and the possibility of being integrated into clinical evaluations to better define walking rehabilitation strategies in a quick and easy way.

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INTRODUCTION: Outcome measures using telerehabilitation (TR) in the context of post-stroke rehabilitation are an area of emerging research. The current review assesses the literature related to TR for patients requiring post-stroke rehabilitation. The purpose of this study is to survey the outcome measures used in TR studies and to define which parts of the International Organization of Functioning are measured in trials. METHODS: TR studies were searched in Cochrane Central Register of Controlled Trials, PubMed, Embase, Scopus, Google Scholar, and Web of Science, The Cochrane Central Register of Controlled Trials (Cochrane Library), the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and the Physiotherapy Evidence Database (PEDro) from 2016 to June 2023. Two reviewers individually assessed the full text. Discrepancies regarding inclusion or exclusion were resolved by an additional reviewer. RESULTS: A total of 24 studies were included in the current review. The findings were synthesized and presented taking into account their implications within clinical practice, areas of investigation, and strategic implementation. CONCLUSIONS: The scoping review has recognized a broad range of outcome measures utilized in TR studies, shedding light on gaps in the current literature. Furthermore, this review serves as a valuable resource for researchers and end users (such as clinicians and policymakers), providing insights into the most appropriate outcome measures for TR. There is a lack of studies examining the required follow-up after TR, emphasizing the need for future research in this area.

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In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training, this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion. The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information. First, electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton. Then, based on muscle synergy theory, a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics. Finally, the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score. The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825, respectively. The results of the fused synergistic features in the K-nearest neighbor (KNN) model yielded higher correlation coefficients ( r = 0.921, P < 0.01). This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results, which provides a basis for the synchronized assessment-training mode of "human in the loop" and provides a potential method for remote rehabilitation training and assessment of the lower extremity.

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BACKGROUND: Although the necessity of upper limb (UL) (dys)function assessment in people with Multiple Sclerosis (pwMS) has been demonstrated in recent years, this is still neglected at an early-stage. OBJECTIVE: The aim of our study was to comprehensively examine bilateral UL in early-stage pwMS who are thought to have no or minimal involvement in activities of daily living for the UL. METHODS: UL muscle strength, sensation and dexterity of 44 pwMS (EDSS score<4, disease duration<5 years, who did not report problems in daily living activities specifically for the UL) were evaluated bilaterally and compared with 44 healthy controls (HC). The relationship between UL function and muscle strength, sensation, cognitive function, fatigue, mood status, participation, EDSS, and disease duration were examined. The results of the outcome measures evaluating the UL function objectively and subjectively were analyzed. RESULTS: Muscle strength, sensation and dexterity were similar in the dominant and nondominant extremities of pwMS and were affected compared to HC. A fair relationship was found between UL function and proximal muscle strength, fatigue, cognitive function, home participation and EDSS. According to the cut-off value (18 s) of Nine Hole Peg Test, only 9.09% of pwMS was unaffected, but 79.54% of affected pwMS had a full ABILHAND score. CONCLUSION: Early-stage pwMS are unaware of the dysfunction since their UL involvement does not affect their daily living activities yet. Patient-reported outcome measures such as ABILHAND can be misleading and have a ceiling effect in the early-stage of the disease. Objective functional evaluations reveal that UL capacity is affected from the early period. Even if pwMS do not report UL involvement, clinicians and researchers should evaluate UL function and include it in the treatment program from an early-stage to prevent further disease burden.

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BACKGROUND: Knee osteoarthritis (KOA) is the most common degenerative joint disease in China, causing a huge economic burden on patients, families, and society. Standardized KOA rehabilitation treatment is an important means to prevent and treat the disease and promote the development of high-quality medical services. This guideline is updated on the basis of the 2016 and 2019 editions. METHODS: Clinical questions regarding rehabilitation assessment and treatment were selected through clinical questions screening and deconstruction, and multiple rounds of Delphi questionnaire consultation. The International Classification of Functioning, Disability and Health (ICF) was used as the theoretical framework, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method was used to grade the quality of evidence and recommendations. RESULTS: The reporting of this guideline followed the standard of Reporting Items for Practice Guidelines in Healthcare (RIGHT). Taking into account patients' preferences and values and the needs of Chinese clinical practice, a total of 11 clinical questions and 28 recommendations were established. The clinical questions were grouped into two categories: KOA assessment (body function, body structure, activity and participation, quality of life, and environmental factors and clinical outcomes assessment, resulting in 9 recommendations) and KOA treatment (health education, therapeutic exercise, therapeutic modalities, occupational therapy, assistive devices, and regenerative rehabilitation approaches, resulting in 19 recommendations). CONCLUSION: This is the first evidence-based guideline for KOA rehabilitation in China utilizing the ICF framework. This guideline provides key guidance for developing systematic, standardized, and precise rehabilitation protocols for KOA across various healthcare settings.

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After regular rehabilitation training, paralysis sequelae can be significantly reduced in patients with limb movement disorders caused by stroke. Rehabilitation assessment is the basis for the formulation of rehabilitation training programs and the objective standard for evaluating the effectiveness of training. However, the quantitative rehabilitation assessment is still in the experimental stage and has not been put into clinical practice. In this work, we propose improved spatial-temporal graph convolutional networks based on precise posture measurement for upper limb rehabilitation assessment. Two Azure Kinect are used to enlarge the angle range of the visual field. The rigid body model of the upper limb with multiple degrees of freedom is established. And the inverse kinematics is optimized based on the hybrid particle swarm optimization algorithm. The self-attention mechanism map is calculated to analyze the role of each upper limb joint in rehabilitation assessment, to improve the spatial-temporal graph convolution neural network model. Long short-term memory is built to explore the sequence dependence in spatial-temporal feature vectors. An exercise protocol for detecting the distal reachable workspace and proximal self-care ability of the upper limb is designed, and a virtual environment is built. The experimental results indicate that the proposed posture measurement method can reduce position jumps caused by occlusion, improve measurement accuracy and stability, and increase Signal Noise Ratio. By comparing with other models, our rehabilitation assessment model achieved the lowest mean absolute deviation, root mean square error, and mean absolute percentage error. The proposed method can effectively quantitatively evaluate the upper limb motor function of stroke patients.

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Identifying human actions from video data is an important problem in the fields of intelligent rehabilitation assessment. Motion feature extraction and pattern recognition are the two key procedures to achieve such goals. Traditional action recognition models are usually based on the geometric features manually extracted from video frames, which are however difficult to adapt to complex scenarios and cannot achieve high-precision recognition and robustness. We investigate a motion recognition model and apply it to recognize the sequence of complicated actions of a traditional Chinese exercise (ie, Baduanjin). We first developed a combined convolutional neural network (CNN) and long short-term memory (LSTM) model for recognizing the sequence of actions captured in video frames, and applied it to recognize the actions of Baduanjin. Moreover, this method has been compared with the traditional action recognition model based on geometric motion features in which Openpose is used to identify the joint positions in the skeletons. Its performance of high recognition accuracy has been verified on the testing video dataset, containing the video clips from 18 different practicers. The CNN-LSTM recognition model achieved 96.43% accuracy on the testing set; while those manually extracted features in the traditional action recognition model were only able to achieve 66.07% classification accuracy on the testing video dataset. The abstract image features extracted by the CNN module are more effective on improving the classification accuracy of the LSTM model. The proposed CNN-LSTM based method can be a useful tool in recognizing the complicated actions.

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Impaired hand function is one of the most frequently persistent consequences of stroke. Throughout the rehabilitation process, physicians consistently monitor patients and perform kinematic evaluations in order to assess their overall progress in motor recovery. The Sollerman Hand Function Test (SHT) is a valuable assessment tool used to evaluate a patient's capacity to engage in daily activities. It holds great importance in the field of medicine as it aids in the assessment of treatment effectiveness. Nevertheless, the requirement for a therapist's physical presence and the use of specialized materials make the test time-consuming and reliant on clinic availability. In this paper, we propose a computer-vision-based approach to the "Write with a pen" sub-test, originally included in the SHT. Our implementation does not require extra hardware equipment and is able to run on lower-end hardware specifications, using a single RGB camera. We have incorporated all the original test's guidelines and scoring methods into our application, additionally providing an accurate hand spasticity evaluator. After briefly presenting the current research approaches, we analyze and demonstrate our application, as well as discuss some issues and limitations. Lastly, we share some preliminary findings from real-world application usage conducted at the University campus and outline our future plans.

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Rehabilitation assessment is the basis and important part of rehabilitation diagnosis and treatment. At present, clinical evaluation is usually carried out by observation method and scale method. At the same time, researchers monitor patients' physical condition data through sensor system and other equipment as a supplement. The purpose of this study is to review the application and development of objective rehabilitation assessment technology in clinical practice, and to discuss its limitations and strategies to provide reference for related research.

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OBJECTIVES: Evidence shows that rehabilitation is the most effective strategy to reduce the disability rate of patients with stroke. However, there is limited understanding about the factors associated with rehabilitation assessment among patients with intracerebral hemorrhage (ICH) in China. We aimed to investigate the factors associated with rehabilitation assessment in patients with ICH and the relationship between rehabilitation assessment and hospitalization outcomes. METHODS: Data from 85,664 patients with ICH admitted to 1,312 hospitals between 1 August 2015 and 31 July 2019 were analyzed. A multivariable logistic regression model accounting for in-hospital clustering was used to identify patient and hospital factors associated with rehabilitation assessment during acute hospitalization. RESULTS: A total of 62,228 (72.6%) patients with ICH underwent rehabilitation assessments. In multivariable analyses, factors associated with an increased likelihood of undergoing a rehabilitation assessment (P < .05) included a higher Glasgow Coma Scale score on admission, a history of hypertension, a history of peripheral vascular disease, dysphagia screening, carotid vessel imaging, and a longer length of hospital stay. Conversely, patients admitted to the intensive care unit and tertiary-grade hospitals were less likely to undergo rehabilitation assessments during hospitalization for ICH. DISCUSSION: This study showed that the rate of rehabilitation assessment was 74.2%, which is low. Rehabilitation assessment was associated with longer hospital stays and lower mortality. Therefore, patients with acute cerebral haemorrhage should undergo comprehensive and professional rehabilitation assessment.

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With a rapidly aging population， there is a huge potential demand for rehabilitation and assistive products and services. Especially in the global context where greater attention is given to disadvantaged groups， there is a need to cater to the needs of the elderly in terms of rehabilitation and assistive aid to improve their quality of life. Compared with developed countries， China’s rehabilitation aid industry is still in its early stages of development and needs to move away from traditional equipment manufacturing and integrate with intelligent manufacturing to provide more development possibilities and choices. International cooperation is expected to become one of the future research and development directions for rehabilitation aids. Taking typical cities and countries at home and abroad as examples， this paper explores the development of rehabilitation aids， and calls for the cultivation of more rehabilitation aid professionals to help more people in need.

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Objective:To design a motor impairment rehabilitation monitoring aid for the assessment of motor impairment in patients who do not have or have difficulty walking independently.Methods:An assistive device vehicle was designed, equipped with an accelerometer and a six-dimensional force sensor. The normal walking (NW) group and abnormal walking group were set up, in which the abnormal walking group included moderate abnormal walking (MA) group, moderate abnormal walking with the aid of the assistive vehicle (MA-V) group, severe abnormal walking (SA) group, and severe abnormal walking with the aid of the assistive vehicle (SA-V) group. In the MA-V group, the range of knee movement was adjusted from 0 to 30°, and in the SA group, the knee joint was completely unbending. The gait cycles, peak and mean acceleration values of the right and left legs were evaluated by accelerometers, and changes in the upper limb forces were assessed by six-dimensional force transducers.Results:For the moderate impairment group, the difference in gait cycle between the MA and MA-V groups was not statistically significant ( P>0.05), and the gait cycle in the MA-V group was slightly greater than that in the MA group. For the severe injury group, the gait cycle of the SA-V group was lower than that of the SA group, and the difference was statistically significant ( P<0.01). For all abnormal groups, the mean and peak acceleration of the left leg were greater than that of the right leg, and the difference between the peak acceleration of the left leg and that of the right leg was statistically significant ( P<0.05). In the abnormal walking pattern, the mean (absolute) value of the left hand force was greater than that of the right hand, especially in the Z-axis. The standard deviations of the combined forces on the left side for the NW, MA-V, and SA-V groups were 2.759, 8.297, and 13.118 N, respectively. The SA-V group had the highest dispersion in the force scatter plot, while the NW group had a better concentration. Conclusions:An assistive vehicle equipped with an accelerometer and a six-dimensional force sensor was designed to help physicians in the assessment and rehabilitation of motor disorders.

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In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training, this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion. The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information. First, electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton. Then, based on muscle synergy theory, a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics. Finally, the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score. The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825, respectively. The results of the fused synergistic features in the K-nearest neighbor (KNN) model yielded higher correlation coefficients ( r = 0.921, P < 0.01). This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results, which provides a basis for the synchronized assessment-training mode of "human in the loop" and provides a potential method for remote rehabilitation training and assessment of the lower extremity.

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Rehabilitation assessment is the basis and important part of rehabilitation diagnosis and treatment. At present, clinical evaluation is usually carried out by observation method and scale method. At the same time, researchers monitor patients' physical condition data through sensor system and other equipment as a supplement. The purpose of this study is to review the application and development of objective rehabilitation assessment technology in clinical practice, and to discuss its limitations and strategies to provide reference for related research.

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