Search

The current rehabilitation evaluation methods for patients with hand dysfunction face issues such as inconsistent standards and incomplete quantification processes. To address these challenges, this paper introduces a rehabilitation evaluation system that integrates various rehabilitation training modes and leverages an exoskeleton finger rehabilitation robot. This system is carefully designed and thoroughly analyzed based on the diverse training modes offered by the rehabilitation robot. Twenty stroke patients and six healthy subjects were recruited to perform grasping of static objects and gesture movement experiments, which were evaluated by Brunnstrom's motor evaluation and rehabilitation evaluation tests, respectively, and the results were compared. The experimental results showed that the results of the robotic rehabilitation evaluation of the 20 patients were consistent with the clinical Brunnstrom motor grades, which verified the accuracy of the rehabilitation evaluation system that was designed in this study. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Physical therapists play a crucial role in guiding patients through effective and safe rehabilitation processes according to medical guidelines. However, due to the therapist-patient imbalance, it is neither economical nor feasible for therapists to provide guidance to every patient during recovery sessions. Automated assessment of physical rehabilitation can help with this problem, but accurately quantifying patients’ training movements and providing meaningful feedback poses a challenge. In this paper, an Expert-knowledge-based Graph Convolutional approach is proposed to automate the assessment of the quality of physical rehabilitation exercises. This approach utilizes experts’ knowledge to improve the spatial feature extraction ability of the Graph Convolutional module and a Gated pooling module for feature aggregation. Additionally, a Transformer module is employed to capture long-range temporal dependencies in the movements. The attention scores and weight matrix obtained through this approach can serve as interpretability tools to help therapists understand the assessment model and assist patients in improving their exercises. The effectiveness of the proposed method is verified on the KIMORE dataset, achieving state-of-the-art performance compared to existing models. Experimental results also illustrate the interpretability of the method in both spatial and temporal dimensions.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Markerless pose estimation based on computer vision provides a simpler and cheaper alternative to human motion capture, with great potential for clinical diagnosis and remote rehabilitation assessment. Currently, the markerless 3D pose estimation is mainly based on multi-view technology, while the more promising single-view technology has defects such as low accuracy and reliability, which seriously limits clinical application. This study proposes a high-resolution graph convolutional multilayer perception (HGcnMLP) human 3D pose estimation framework for smartphone monocular videos and estimates 15 healthy adults and 12 patients with musculoskeletal disorders (sarcopenia and osteoarthritis) gait spatiotemporal, knee angle, and center-of-mass (COM) velocity parameters, etc., and compared with the VICON gold standard system. The results show that most of the calculated parameters have excellent reliability (VICON, ICC (2, k): 0.853–0.982; Phone, ICC (2, k): 0.839–0.975) and validity (Pearson r: 0.808–0.978, p

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.

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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.

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Introduction: Stroke is usually accompanied by a range of complications, like post-stroke motor disorders. So far, its evaluation of motor function is developed on clinical scales, such as Fugl-Meyer Assessment (FMA), Instrumental Activities of Daily Living (IADL), etc. These scale results from behavior and kinematic assessment are inevitably influenced by subjective factors, like the experience of patients and doctors, lacking neurological correlations and evidence. Methods: This paper applied a microstate model based on modified k-means clustering to analyze 64-channel electroencephalogram (EEG) from 12 stroke patients and 12 healthy volunteers, respectively, to explore the feasibility of applying microstate analysis to stroke patients. We aimed at finding some possible differences between stroke and healthy individuals in resting-state EEG microstate features. We further explored the correlations between EEG microstate features and scales within the stroke group. Results and discussion: By statistical analysis, we obtained significant differences in EEG microstate features between the stroke and healthy groups and significant correlations between microstate features and scales within the stroke group. These results might provide some neurological evidence and correlations in the perspective of EEG microstate analysis for post-stroke rehabilitation and evaluation of motor disorders. Our work suggests that microstate analysis of resting-state EEG is a promising method to assist clinical and assessment applications. [ABSTRACT FROM AUTHOR]

Ageing populations are becoming a global issue. Against this background, the assessment and treatment of geriatric conditions have become increasingly important. This study draws on the multisensory integration of virtual reality (VR) devices in the field of rehabilitation to assess brain function in young and old people. The study is based on multimodal data generated by combining high temporal resolution electroencephalogram (EEG) and subjective scales and behavioural indicators reflecting motor abilities. The phase locking value (PLV) was chosen as an indicator of functional connectivity (FC), and six brain regions, namely LPFC, RPFC, LOL, ROL, LMC and RMC, were analysed. The results showed a significant difference in the alpha band on comparing the resting and task states in the younger group. A significant difference between the two states in the alpha and beta bands was observed when comparing task states in the younger and older groups. Meanwhile, this study affirms that advancing age significantly affects human locomotor performance and also has a correlation with cognitive level. The study proposes a novel accurate and valid assessment method that offers new possibilities for assessing and rehabilitating geriatric diseases. Thus, this method has the potential to contribute to the field of rehabilitation medicine.

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.

Copyright of Journal of Zhejiang University: Science B is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ageing populations are becoming a global issue. Against this background, the assessment and treatment of geriatric conditions have become increasingly important. This study draws on the multisensory integration of virtual reality (VR) devices in the field of rehabilitation to assess brain function in young and old people. The study is based on multimodal data generated by combining high temporal resolution electroencephalogram (EEG) and subjective scales and behavioural indicators reflecting motor abilities. The phase locking value (PLV) was chosen as an indicator of functional connectivity (FC), and six brain regions, namely LPFC, RPFC, LOL, ROL, LMC and RMC, were analysed. The results showed a significant difference in the alpha band on comparing the resting and task states in the younger group. A significant difference between the two states in the alpha and beta bands was observed when comparing task states in the younger and older groups. Meanwhile, this study affirms that advancing age significantly affects human locomotor performance and also has a correlation with cognitive level. The study proposes a novel accurate and valid assessment method that offers new possibilities for assessing and rehabilitating geriatric diseases. Thus, this method has the potential to contribute to the field of rehabilitation medicine. [ABSTRACT FROM AUTHOR]

Upper extremity impairments are a common consequence of stroke, necessitating thorough rehabilitation monitoring and kinematic assessments to facilitate motor recovery. The Box and Block Test (BBT) and Sollerman Hand Function Test (SHFT) are two widely utilized and recommended tools for objectively measuring upper limb dexterity and evaluating fine motor skill rehabilitation in patients. However, these tests rely on specific equipment and therapist attendance, making the process time-consuming and clinic-dependent. This paper introduces a computer vision-based hand rehabilitation assessment suite specifically designed for mobile devices, such as smartphones and tablets, which serves as a virtual alternative to traditional methods while also incorporating an interactive exergame. Our application faithfully integrates the original tests' guidelines and procedures into an engaging computer vision experience, utilizing advanced technologies like MediaPipe Hands for precise hand and finger tracking. This innovative solution obviates the need for additional computer peripherals such as smart gloves or VR headsets, as well as physical equipment like wooden boxes and blocks, relying solely on the built-in camera of everyday mobile devices. In addition, we address several technical challenges encountered in our approach and outline future directions for score normalization and feature expansion, ensuring the continued improvement and efficacy of our hand rehabilitation assessment suite. [ABSTRACT FROM AUTHOR]

[Display omitted] • Nanostructured MnOx/MoS 2 electrode facilitates capacitance change of all-fiber ITSs. • Cotton fibers boost ionic distribution to enhance mechanical stability. • Rapid response and exceptional sensitivity under high pressure. • Monitoring of human movement and physiological signals for sensors are achieved. • Integration with motion recognition technology enhances rehabilitation assessment. Interfacial capacitance sensing technology shows great potential in the fields of flexible electronics and healthcare. However, further advancements are necessary to enhance the sensitivity and mechanical stability of all-fiber iontronic tactile sensors (ITSs), especially under high pressure. This study addresses these challenges by optimizing the electrode structure and composition, incorporating hydrophilic cotton fibers as the dielectric layer framework. As a result, the ITSs exhibit ultrahigh sensitivity (58853 kPa−1), rapid response times (below 2 ms), and improved mechanical stability, enduring 10,000 cycles at 195.2 kPa. Moreover, these sensors have proven effective in detecting human motion and physiological signals in both air and aquatic environments. Integration into a motion state recognition system demonstrates potential applications in sports injury recovery, stroke rehabilitation, and chronic pain management. [ABSTRACT FROM AUTHOR]

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.

Background and purpose: People with multiple sclerosis (PwMS) often report walking limitations even when the gold standard Expanded Disability Status Scale (EDSS) indicates normal walking endurance/autonomy. The present multicenter study on early‐stage PwMS aims at analyzing which aspects are associated with patient‐reported walking limitations measured with the 12‐item Multiple Sclerosis Walking Scale (MSWS‐12). Methods: Eighty‐two PwMS (EDSS ≤ 2.5) were assessed using the Fullerton Advanced Balance Scale—short (FAB‐s), the Fatigue Severity Scale (FSS) and the 6‐min Walk Test (6MWT), the latter administered also to 21 healthy subjects. Participants performed the 6MWT wearing three inertial sensors on ankles and trunk. Instrumented metrics describing gait velocity (stride length and frequency) and quality (regularity, symmetry, instability) were computed from sensor data. Fatigue (FSS), balance (FAB‐s), walking endurance (6MWT) and instrumented metrics were entered in a multiple regression model with MSWS‐12 as dependent variable. Results: Gait symmetry, gait instability, fatigue and balance were significantly associated with self‐rated walking ability, whilst walking endurance and velocity were not. Fatigue, balance, gait symmetry and instability were more impaired in participants reporting mild‐to‐moderate (MSMM‐PWL, 25 ≤ MSWS‐12 < 75) compared to those reporting none‐to‐minimal (MSnm‐PWL, 0 ≤ MSWS‐12 ≤ 25) perceived walking limitations. Compared to healthy subjects, gait symmetry and stability were reduced in MSnm‐PWL and MSMM‐PWL, even in those participants with EDSS ≤ 1.5. Conclusion: Instrumentally assessed gait quality aspects (symmetry and instability) are associated with patient‐reported walking ability in early‐stage PwMS and seem sensitive biomarkers to detect subtle impairments even in the earliest stages of the disease (EDSS ≤ 1.5). Future studies should assess their ability to follow walking change due to MS progression or pharmacological/rehabilitation interventions. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Bai, Li, Lu and Wen.)

Markerless pose estimation based on computer vision provides a simpler and cheaper alternative to human motion capture, with great potential for clinical diagnosis and remote rehabilitation assessment. Currently, the markerless 3D pose estimation is mainly based on multi-view technology, while the more promising single-view technology has defects such as low accuracy and reliability, which seriously limits clinical application. This study proposes a high-resolution graph convolutional multilayer perception (HGcnMLP) human 3D pose estimation framework for smartphone monocular videos and estimates 15 healthy adults and 12 patients with musculoskeletal disorders (sarcopenia and osteoarthritis) gait spatiotemporal, knee angle, and center-of-mass (COM) velocity parameters, etc., and compared with the VICON gold standard system. The results show that most of the calculated parameters have excellent reliability (VICON, ICC (2, k): 0.853-0.982; Phone, ICC (2, k): 0.839-0.975) and validity (Pearson r: 0.808-0.978, p < 0.05). In addition, the proposed system can better evaluate human gait balance ability, and the K-means++ clustering algorithm can successfully distinguish patients into different recovery level groups. This study verifies the potential of a single smartphone video for 3D human pose estimation for rehabilitation auxiliary diagnosis and balance level recognition, and is an effective attempt at the clinical application of emerging computer vision technology. In the future, it is hoped that the corresponding smartphone program will be developed to provide a low-cost, effective, and simple new tool for remote monitoring and rehabilitation assessment of patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hu, Diao, Wang, Li, He, Ning, Lou, Li and Zhao.)

The purpose of the research is the scientific justification and development of a system of physical therapy for children who need palliative care. The methods of research in this case are empirical methods (description, comparison, measurement), the method of theoretical knowledge (axiomatic), general logical methods (analysis, synthesis), systematic approach. The basis of providing palliative care involves a patient-centered approach of a multidisciplinary team, the composition of which is determined by the patient's needs, his age, and the severity of his condition. If we consider the rehabilitation support of children who need non-oncological palliative care within the framework of the International Classification of Functioning, it can be stated that, having violations in the Body Structure and Functions component, they have significant limitations in the Activity-Participation component. Contextual factors – Environmental factors and Personal factors – play a significant role in the quality and effectiveness of rehabilitation. After the rehabilitation instrumental examination, the needs of the child and his family members, child-centered tasks are identified, and the goal of physical therapy is formed. The means of the rehabilitation program are therapeutic physical culture, massage, positioning, dance and movement therapy, photo therapy and art therapy using the «hand in hand» method; selection of technical means of movement, rehabilitation equipment for modification of the educational space. Therefore, the means of the physical therapy program will be directed to reducing the complication of the main disease, will help to form the household-educational-social organization of the space of children of this category, will contribute to the emotional and psychological stability of patients, their family members, and representatives of the interdisciplinary team.

Improving health is an important driving factor of sensor technology applications. To meet the demands of precision medicine for medical rehabilitation and elderly guardianship, using wearable sensors to get kinematics, kinetics, and biochemical information has become an interdisciplinary research hotspot recently. This paper proposed a low-cost, intelligent, and lightweight wearable platform for rehabilitation assessment in stroke victims with gait impairment. The paper starts from the sensor physical properties and human physiology structure, and aims to solve sensor drift problem by zero velocity update algorithm. A complementary filter based on proportional integral controller was adopted to eliminate computational errors. Preliminary clinical gait experiments results showed that the protocol we have designed according to the proposed guidelines had demonstrated to be operatively simple and efficient compared with existing methods. The expectation of this case study is to develop a dedicated tool for supporting diagnosis and rehabilitation in the hospital.

• The number of studies focusing on the upper limb in the early-stage of the disease from a comprehensive perspective is limited although upper limb dysfunction is common in MS. • Our study showed that early-stage pwMS who did not report upper limb involvement had bilateral dysfunction, and performance perceived by the patient could be misleading. • Clinicians and researchers should include the upper limb in the treatment program from an early-stage to avoid further disease burden, even though no involvement has been reported. 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. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

In the process of rehabilitation, the objectivity and the accuracy of rehabilitation assessment have an obvious impact on the follow-up training. To improve this problem, using a multi-sensor source, this paper attempts to establish a comprehensive assessment method of the finger rehabilitation effect, including three indicators of finger muscle strength, muscle fatigue degree, and range of motion. Firstly, on the basis of the fingertip pressure sensor of the End-Effector Finger Rehabilitation Robot, a mathematical model of finger muscle strength estimation was established, and the estimated muscle strength was scored using the entropy weight method. Secondly, using an sEMG signal sensor, a fatigue monitoring system was designed in the training process, and the fatigue degree was determined on the basis of the change trend of the eigenvalues of MAV and RMS. Lastly, a human–machine motion coupling model was established, and the joint range of motion acquisition and scoring model were obtained on the basis of the motor encoder. According to the above three indicators, using the AHP assessment method to establish a comprehensive rehabilitation assessment method, the effectiveness of the method was verified by experiments. This paper provides a potential new idea and method for objective, accurate, and convenient assessment of finger function rehabilitation, which is of positive significance for alleviating the burden on rehabilitation doctors and improving rehabilitation efficiency.

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.

Highlights • An object-based method was developed to map woody cover from UAV multispectral data. • Proportional woody cover derived from the method matched manually-derived estimates. • The technique works on data from different sensors and processing methods. • Results can be used to monitor revegetation efforts on mine sites. Abstract Revegetation success is a key element of mine site rehabilitation. A number of criteria related to mine site close-out are associated with revegetation. The monitoring of mine site revegetation efforts have traditionally been undertaken using field-based plot or transect methods. Often the sampling design for this monitoring is limited due to resource constraints, therefore reducing the statistical power of the data and missing information over most of the mine site. The recent advances in Remotely Piloted Aircraft Systems (RPAS) technology for remote sensing enables the collection of appropriate scale data over entire mine sites reducing the need for sampling and eliminating potential bias. This paper describes an object-based technique for extracting woody cover and estimating proportional woody cover from RPAS imagery over the rehabilitated Jabiluka mine site located in the tropical north of Australia. The technique was tested on three data sets that covered three different dates, two different sensors, and two different processing methods. Overall woody cover detection accuracies from each data set were over 95%. Proportional woody cover derived from the technique showed strong linear relationships with manually estimated cover (r 2 > 0.88). This study shows that the technique is robust and works with a range of RPAS data sets and enables at scale analysis of woody cover change between dates. The technique will be an important component of ongoing monitoring of mine site revegetation in the region. [ABSTRACT FROM AUTHOR]

Post-stroke patients very commonly present upper limb deficits, while their rehabilitation comprises regular monitoring and kinematic assessments to evaluate motor recovery. The Box and Block Test (BBT), as well as the Sollerman Hand function Test (SHT), are two of the most used and recommended tools to objectively measure upper limb dexterity, in addition to evaluating the rehabilitation of fine motor skills in patients. However, the tests themselves require the use of very specific equipment, along with the physical attendance of a therapist, making the whole procedure time consuming and clinic dependent. This paper proposes a computer vision hand rehabilitation assessment suite, which stands as a virtual alternative to the real-world scenarios. Our application integrates all the original tests' guidelines and procedures into an interactive computer vision experience that utilizes bleeding edge technologies such as MediaPipe Hands for hand and finger tracking. This innovative tool requires neither any additional computer peripherals (smart gloves, VR headsets) nor any kind of extra physical equipment (wooden box, blocks), but works instead with just a mid-range PC and a camera. Our system can be deployed in residential spaces via modern 5G/6G or FTTx networks, and the test results can be sent remotely to any physician or rehabilitation expert. Finally, we shortly discuss some technical issues of our approach, as well as present some future directions regarding our tool's score normalization and feature expansion. [ABSTRACT FROM AUTHOR]

Human gait reflects health condition and is widely adopted as a diagnostic basisin clinical practice. This research adopts compact inertial sensor nodes to monitor the functionof human lower limbs, which implies the most fundamental locomotion ability. The proposedwearable gait analysis system captures limb motion and reconstructs 3D models with high accuracy.It can output the kinematic parameters of joint flexion and extension, as well as the displacementdata of human limbs. The experimental results provide strong support for quick access to accuratehuman gait data. This paper aims to provide a clue for how to learn more about gait postureand how wearable gait analysis can enhance clinical outcomes. With an ever-expanding gait database,it is possible to help physiotherapists to quickly discover the causes of abnormal gaits, sports injuryrisks, and chronic pain, and provides guidance for arranging personalized rehabilitation programsfor patients. The proposed framework may eventually become a useful tool for continually monitoringspatio-temporal gait parameters and decision-making in an ambulatory environment.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Bai, Wang, Lu and Wen.)

Gait and posture are regular activities which are fully controlled by the sensorimotor cortex. In this study, fluctuations of joint angle and asymmetry of foot elevation in human walking stride records are analyzed to assess gait in healthy adults and patients affected with gait disorders. This paper aims to build a low-cost, intelligent and lightweight wearable gait analysis platform based on the emerging body sensor networks, which can be used for rehabilitation assessment of patients with gait impairments. A calibration method for accelerometer and magnetometer was proposed to deal with ubiquitous orthoronal error and magnetic disturbance. Proportional integral controller based complementary filter and error correction of gait parameters have been defined with a multi-sensor data fusion algorithm. The purpose of the current work is to investigate the effectiveness of obtained gait data in differentiating healthy subjects and patients with gait impairments. Preliminary clinical gait experiments results showed that the proposed system can be effective in auxiliary diagnosis and rehabilitation plan formulation compared to existing methods, which indicated that the proposed method has great potential as an auxiliary for medical rehabilitation assessment. [ABSTRACT FROM AUTHOR]