Rehabilitation Of Stroke Patients Research Articles

Combining Transcranial Direct Current Stimulation With Hand Robotic Rehabilitation in Chronic Stroke Patients: A Double-Blind Randomized Clinical Trial.

This study aimed to assess the impact of combining transcranial direct current stimulation with end-effector robot-assisted treatment on upper limb function, spasticity, and hand dexterity in chronic stroke patients. This was a prospective, double-blind randomized trial with 20 equally allocated stroke patients. The experimental group received dual transcranial direct current stimulation (anode over affected M1, cathode over contralateral M1) alongside robot-assisted treatment, while the control group received sham transcranial direct current stimulation with the same electrode placement + robot-assisted treatment. Each patient underwent 20 combined transcranial direct current stimulation and robot-assisted treatment sessions. The primary outcome measure was the Fugl-Meyer Upper Limb motor score, with secondary outcomes including AMADEO kinematic measures, Action Research Arm Test, and Functional Independence Measure. Assessments were conducted at baseline, after rehabilitation, and 3 mos later. Combining bilateral transcranial direct current stimulation with robot-assisted treatment did not yield additional improvements in Fugl-Meyer Upper Limb motor score, Functional Independence Measure, or Action Research Arm Test scores among stroke patients. However, the real transcranial direct current stimulation group showed enhanced finger flexion in the affected hand based on AMADEO kinematic measures. The addition of transcranial direct current stimulation to robot-assisted treatment did not result in significant overall functional improvements in chronic stroke patients. However, a benefit was observed in finger flexion of the affected hand.

Electric field simulation and appropriate electrode positioning for optimized transcranial direct current stimulation of stroke patients: an in Silico model

Transcranial Direct Current Stimulation (tDCS) has benefits for motor rehabilitation in stroke patients, but its clinical application is limited due to inter-individual heterogeneous effects. Recently, optimized tDCS that considers individual brain structure has been proposed, but the utility thereof has not been studied in detail. We explored whether optimized tDCS provides unique electrode positions for each patient and creates a higher target electric field than the conventional approach. A comparative within-subject simulation study was conducted using data collected for a randomized controlled study evaluating the effect of optimized tDCS on upper extremity function in stroke patients. Using Neurophet tES LAB 3.0 software, individual brain models were created based on magnetic resonance images and tDCS simulations were performed for each of the conventional and optimized configurations. A comparison of electrode positions between conventional tDCS and optimized tDCS was quantified by calculation of Euclidean distances. A total of 21 stroke patients were studied. Optimized tDCS produced a higher electric field in the hand motor region than conventional tDCS, with an average improvement of 20% and a maximum of 52%. The electrode montage for optimized tDCS was unique to each patient and exhibited various configurations that differed from electrode placement of conventional tDCS. Optimized tDCS afforded a higher electric field in the target of a stroke patient compared to conventional tDCS, which was made possible by appropriately positioning the electrodes. Our findings may encourage further trials on optimized tDCS for motor rehabilitation after stroke.

Reminiscent music therapy combined with robot-assisted rehabilitation for elderly stroke patients: a pilot study

BackgroundAlthough some studies suggest that robot-assisted technology can significantly improve upper limb function in stroke patients compared to traditional rehabilitation training, it is still necessary to incorporate an auxiliary intervention to alleviate negative emotions, thereby alleviating the post-stroke fatigue and encouraging patients to actively respond to rehabilitation. However, the effect of the auxiliary intervention is unknown.ObjectiveTo evaluate the effect of reminiscent music therapy combined with robot-assisted rehabilitation in elderly patients with upper limb dysfunction.MethodsFrom November 2022 to March 2023, elderly patients with upper limb dysfunction after stroke were assigned to one of three groups, with group A receiving usual rehabilitation treatment and care plus robot-assisted rehabilitation and reminiscent music therapy, group B receiving usual rehabilitation treatment and care plus robot-assisted rehabilitation, and group C receiving only usual rehabilitation treatment and care. Thirty patients completed this study, with 10 participants in each group. Activities of daily living, self-esteem, rehabilitation self-efficacy, positive emotion and upper limb function were measured before and after the intervention. One-way analysis of variance, paired-sample t-test, Kruskal-Wallis H test, Wilcoxon signed rank sum test and Chi-square test were used to analyze the data.ResultsAccording to the intragroup comparisons, in the three groups, all outcome measurements were significantly higher than those at baseline (all P < 0.05). After the intervention, the differences in the self-management effectiveness, rehabilitation self-efficacy, and positive emotion score were statistically significant among the three groups (all P < 0.05). In accordance with the results of Bonferroni analysis, the self-management effectiveness score of group A was significantly higher than that of Group B and Group C (all P < 0.05). The rehabilitation self-efficacy score of group A was significantly higher than that of Group B and Group C (P < 0.05). The positive emotion score of group A was significantly higher than that of Group B and Group C (P < 0.05).ConclusionReminiscent music therapy combined with robot-assisted rehabilitation is a promising approach to improve rehabilitation self-efficacy and positive emotion, which is evidence that reminiscent music therapy may be an effective auxiliary intervention to improve rehabilitation outcomes.

A Low Cost of an Exoskeleton Finger for Stroke Patient

Hand motor impairment poses a significant obstacle for stroke survivors, profoundly affecting their capacity to perform crucial daily tasks. This impairment frequently includes an inability to flex fingers effectively. This research is dedicated to the development of a low-cost exoskeleton finger tailored for stroke patient rehabilitation. The proposed exoskeleton finger boasts characteristics of being lightweight, affordable, and straightforward to manufacture. Moreover, it prioritizes portability and user-friendliness. The design integrates PLA material, springs, servo motors, and Arduino Uno technology. The study introduces two distinct exoskeleton finger designs, with one of them incorporating a glove component. Both exoskeleton finger variations comprise three degrees of freedom (DOF), three links, and two joints. The findings show that the exoskeleton finger's ability to effectively maneuver a paralyzed finger into different positions as commanded, facilitating the rehabilitation process. Furthermore, both designs exhibit reliability in aiding the opening and closing exercises of impaired fingers.

Synergetic gait prediction and compliant control of SEA-driven knee exoskeleton for gait rehabilitation.

In recent years, lower limb exoskeletons have achieved satisfactory clinical curative effects in rehabilitating stroke patients. Furthermore, generating individualized trajectories for each patient and avoiding secondary injury in rehabilitation training are important issues. This paper explores the utilization of series elastic actuator (SEA) to deliver compliant force and enhance impact resistance in human-robot interaction, and we present the design of novel knee exoskeleton driven by SEA. Subsequently, the novel gait trajectory prediction method and compliant control method are proposed. The attention-based CNN-LSTM model is established to generate personalized gait trajectories for affected limbs, in which the spatial-temporal attention mechanism is adopted to improve the prediction accuracy. The compliant control strategy is proposed to nonlinearly and adaptively tune impedance parameters based on artificial potential field (APF) method, and active rehabilitation training is carried out in the coordination space to guarantee patient safety. The experimental results based on four healthy subjects demonstrated that synergetic gait prediction model could satisfactorily characterize the coordination movement with higher accuracy. The compliant control could limit the patient's movement in the safe coordination tunnel while considering personalization and flexibility.

Barriers and Facilitators to Aerobic Exercise Testing Practices of Physiotherapists in In-Patient Stroke Rehabilitation Settings Across Canada: A Theory-Informed Web-Based Survey

Background and Purpose: Stroke best practice guidelines recommend aerobic exercise (AEx) testing once patients post-stroke are medically stable and before initiating AEx training. This study describes current AEx testing practice of physiotherapists (PTs) in in-patient stroke rehabilitation (SR) and the barriers and facilitators to this practice. Methods: We conducted a cross-sectional web-based survey of registered PTs working in Canadian in-patient SR settings, guided by the Theoretical Domains Framework (TDF). Results: Fifteen of 37 (41%) participants reported conducting AEx testing with people post-stroke. A field test (92%) involving walking was the most commonly used test type. Barriers and facilitators to AEx testing practice from all 14 TDF domains were endorsed. While 73% ( n = 19/26) participants recognized that AEx testing supports therapy goals, over 60% reported lacking maximal and submaximal AEx testing knowledge and skills, and 58% did not perceive AEx testing to be an organizational priority due to the focus on function and mobility during in-patient SR. Discussion and Conclusion: Less than half of participants performed AEx testing despite recognizing its value for people post-stroke. Predominantly, practitioner- and organisation-related factors influenced participants’ use of AEx testing in in-patient SR. An understanding of how PTs can navigate the complex barriers to AEx testing is needed.

Effects of robotic-assisted gait training on physical capacity, and quality of life among chronic stroke patients: A randomized controlled study

BackgroundEven though robotic therapy is becoming more commonly used in research protocols for lower limb stroke rehabilitation, there still is a significant gap between research evidence and its use in clinical practice. Therefore, the present study was designed assuming that the wearable mobile gait device training for chronic stroke patients might have different effects on functional independence when compared to training with a stationary gait device. The present study aims to examine the effects of gait training with ExoAthlet exoskeleton and Lokomat Free-D on functional independence, functional capacity, and quality of life in chronic stroke patients. MethodsThe present study included 32 chronic stroke patients. Participants were randomly divided into two groups. Functional independence of patients was evaluated by using Functional Independence Measure (FIM), physical function was assessed by using the 30-second chair stand test (30-CST), functional capacity was measured by using the 6-Minute Walk Test (6MWT), and quality of life was assessed by using Short Form 36 (SF36). All participants underwent a conventional physiotherapy program for eight weeks, three sessions per week, and each session lasted 60 min. After the physiotherapy program, one group received gait training by using ExoAthlet exoskeleton (ExoAtlet 1 model/2019, Russia), while the other group received training by using Lokomat Free-D (Hocoma, Lokomat Pro Free-D model/2015, Switzerland). Participants were assessed at baseline and post-intervention. ResultsResults achieved in this study revealed that there was a statistically significant difference between FIM, 30-CST, 6MWT, and SF36 scores before and after the treatment in both groups (p < 0.05).There was no difference in FIM, 30-CST, and 6MWT results between Exoskeleton ExoAthlet and Lokomat Free-D groups (p > 0.05). However, there was a statistically significant difference between Exoskeleton ExoAthlet and Lokomat Free-D groups in terms of SF-36 sub-parameters “vitality”, “mental health”, “bodily pain”, and “general health perception” (p < 0.05). ConclusionsThis study demonstrated that the use of ExoAthlet exoskeleton and Lokomat Free-D in addition to conventional physiotherapy, was effective in improving functional independence, physical function, functional capacity, and quality of life among chronic stroke patients. Incorporation of robotic gait aids into rehabilitation for chronic stroke patients might offer significant advantages.

Topical issues of nutritional support for patients with acute cerebrovascular accident

The incidence of malnutrition in stroke patients varies widely and amounts to 6.1–62%. Risk factors for malnutrition in stroke patients are diverse and include the type and severity of stroke, gender, age, dysphagia, cognitive impairment, polysensory insufficiency, severe comorbid conditions, lack of adequate care, etc. At the same time, both the previous and the malnutrition that developed as a result of the stroke is the reason for a longer stay in the hospital, deterioration of functional results and an increase in mortality rates. The metabolic response to stress in stroke patients is represented by the hypermetabolism-hypercatabolism syndrome and is an essential component of the systemic inflammatory response, the development and progression of which leads to infectious complications, multiple organ dysfunction, increased hospitalization and increased mortality. Currently, clinical nutrition, or nutritional support (NS), is considered not only as a process of providing the body with energy and plastic material for the prevention and treatment of malnutrition, but also as an opportunity to influence structural, functional and metabolic processes in order to increase the adaptive reserves of the body, which is necessary for the recovery and rehabilitation of patients. The objectives of this review are the analysis of modern scientific data on clinical nutrition strategies and the development of an optimal algorithm of actions for the implementation of NS in clinical practice in the treatment and rehabilitation of stroke patients.

Early Prediction of Poststroke Rehabilitation Outcomes Using Wearable Sensors.

Inpatient rehabilitation represents a critical setting for stroke treatment, providing intensive, targeted therapy and task-specific practice to minimize a patient's functional deficits and facilitate their reintegration into the community. However, impairment and recovery vary greatly after stroke, making it difficult to predict a patient's future outcomes or response to treatment. In this study, the authors examined the value of early-stage wearable sensor data to predict 3 functional outcomes (ambulation, independence, and risk of falling) at rehabilitation discharge. Fifty-five individuals undergoing inpatient stroke rehabilitation participated in this study. Supervised machine learning classifiers were retrospectively trained to predict discharge outcomes using data collected at hospital admission, including patient information, functional assessment scores, and inertial sensor data from the lower limbs during gait and/or balance tasks. Model performance was compared across different data combinations and was benchmarked against a traditional model trained without sensor data. For patients who were ambulatory at admission, sensor data improved the predictions of ambulation and risk of falling (with weighted F1 scores increasing by 19.6% and 23.4%, respectively) and maintained similar performance for predictions of independence, compared to a benchmark model without sensor data. The best-performing sensor-based models predicted discharge ambulation (community vs household), independence (high vs low), and risk of falling (normal vs high) with accuracies of 84.4%, 68.8%, and 65.9%, respectively. Most misclassifications occurred with admission or discharge scores near the classification boundary. For patients who were nonambulatory at admission, sensor data recorded during simple balance tasks did not offer predictive value over the benchmark models. These findings support the continued investigation of wearable sensors as an accessible, easy-to-use tool to predict the functional recovery after stroke. Accurate, early prediction of poststroke rehabilitation outcomes from wearable sensors would improve our ability to deliver personalized, effective care and discharge planning in the inpatient setting and beyond.

Process mining and customer journey mapping in healthcare: Enhancing patient-centred care in stroke rehabilitation.

Patient-centred care and enhancing patient experience is a priority across Australia. Stroke rehabilitation has multiple consumer touchpoints that would benefit from a better understanding of customer journeys, subsequently impacting better patient-centred care, and contributing to process improvements and better patient outcomes. Customer journey mapping through process mining extracts process data from event logs in existing information systems discovering patient journeys, which can be utilized to monitor guideline compliance and uncover nonconformance. Utilizing process mining and variant analysis, customer journey maps were developed for 130 stroke rehabilitation patients from referral to discharge. In total, 168 cases from the Australasian Rehabilitation Outcomes Centre dataset were matched with 6291 cases from inpatient stroke data. Variants were explored for age, gender, outcome measures, length of stay and functional independence measure (FIM) change. The study illustrated the process, process variants and patient journey map in stroke rehabilitation. Process characteristics of stroke rehabilitation patients were extracted and represented utilizing process mining and results highlighted process variation, attributes, touchpoints and timestamps across stroke rehabilitation patient journeys categorized by patient demographics and outcome variables. Patients demonstrated a mean and median duration of 49.5 days and 44 days, respectively, across the patient journeys. Nine variants were discovered, with 78.46% (n = 102) of patients following the expected sequence of activities in their stroke rehabilitation patient journey. Relationships involving age, gender, length of stay and FIM change along the patient journeys were evident, with four cases experiencing stroke rehabilitation journeys of more than 100 days, warranting further investigation. Process mining can be utilized to visualize and analyse patient journeys and identify gaps in service quality, thus contributing to better patient-centred care and improved patient outcomes and experiences in stroke rehabilitation.

A Novel Low-Pressure Robotic Glove Based on CT-Optimized Finger Joint Kinematic Model for Long-Term Rehabilitation of Stroke Patients.

Wearing robotic gloves has become increasingly crucial for hand rehabilitation in stroke patients. However, traditional robotic gloves can exert additional pressure on the hand, such as prolonged use leading to poor blood circulation and muscle stiffness. To address these concerns, this work analyzes the finger kinematic model based on computerized tomography (CT) images of human hands, and designs a low-pressure robotic glove that conforms to finger kinematic characteristics. Firstly, physiological data on finger joint flexion and extension were collected through CT scans. The equivalent rotation centers of finger joints were obtained using the SURF and RANSAC algorithms. Furthermore, the trajectory of finger joint end and the correlation equation of finger joint motion were fitted, and a comprehensive finger kinematic model was established. Based on this finger kinematic model, a novel under-actuated exoskeleton mechanism was designed using a human-machine integration approach. The novel robotic glove fully aligns with the equivalent rotation centers and natural motion trajectories of the fingers, exerting minimal and evenly distributed dynamic pressure on the fingers, with a theoretical static pressure value of zero. Experiments involving gripping everyday objects demonstrated that the novel robotic glove significantly reduces the overall pressure on the fingers during grasping compared to the pneumatic glove and the traditional exoskeleton robotic glove. It is suitable for long-term use by stroke patients for rehabilitation training.

Corticomuscular Coupling Analysis in Stroke Rehabilitation Based on Variational Mode Decomposition-Transfer Entropy.

This study aims to explore alterations in corticomuscular and cortical coupling during the rehabilitation of stroke patients. We initiated the analysis by employing variational modal decomposition (VMD) on electromyography (EMG) data, followed by the application of VDM-transfer entropy (VMD-TE) to quantify the coupling strength between electroencephalogram (EEG) and EMG signals. Subsequently, we constructed the VMD-TE connection matrix and analyzed the clustering coefficient and small-world attributes within the cortico-muscular functional network (CMFN). Finally, a random forest algorithm was employed to extract features from the VMD-TE connection matrix across different rehabilitation periods. Beta waves in EEG were emerged as the key information carrier between the cortex and muscle, and the CMFN of patients with the beta frequency band has small-world characteristics. During rehabilitation, we observed a decrease in coupling between the initially affected motor cortex and muscle, accompanied by an increase in coupling between the frontal region and muscle. Our findings suggest potential neuro-remodeling in stroke patients after rehabilitation, with CFMN serving as a valuable metric for assessing cortico-muscular coupling.

Event-Related EEG Desynchronization Reveals Enhanced Motor Imagery From the Third Person Perspective by Manipulating Sense of Body Ownership With Virtual Reality for Stroke Patients.

Virtual reality (VR)-based rehabilitation training holds great potential for post-stroke motor recovery. Existing VR-based motor imagery (MI) paradigms mostly focus on the first-person perspective, and the benefit of the third-person perspective (3PP) remains to be further exploited. The 3PP is advantageous for movements involving the back or those with a large range because of its field coverage. Some movements are easier to imagine from the 3PP. However, the 3PP training efficiency may be unsatisfactory, which may be attributed to the difficulty encountered when generating a strong sense of ownership (SOO). In this work, we attempt to enhance a visual-guided 3PP MI in stroke patients by eliciting the SOO over a virtual avatar with VR. We propose to achieve this by inducing the so-called out-of-body experience (OBE), which is a full-body illusion (FBI) that people misperceive a 3PP virtual body as his/her own (i.e., generating the SOO to the virtual body). Electroencephalography signals of 13 stroke patients are recorded while MI of the affected upper limb is being performed. The proposed paradigm is evaluated by comparing event-related desynchronization (ERD) with a control paradigm without FBI induction. The results show that the proposed paradigm leads to a significantly larger ERD during MI, indicating a bilateral activation pattern consistent with that in previous studies. In conclusion, 3PP MI can be enhanced in stroke patients by eliciting the SOO through induction of the "OBE" FBI. This study offers more possibilities for virtual rehabilitation in stroke patients and can further facilitate VR application in rehabilitation.

NeuroRehabilitation OnLine: Description of a regional multidisciplinary group telerehabilitation innovation for stroke and neurological conditions using the Template for Intervention Description and Replication checklist.

Providing recommended amounts of rehabilitation for stroke and neurological patients is challenging. Telerehabilitation is viable for delivering rehabilitation and an acceptable adjunct to in-person therapy. NeuroRehabilitation OnLine (NROL) was developed as a pilot and subsequently operationalised as a regional innovation embedded across four National Health Service (NHS) Trusts. To describe the NROL innovation to assist future implementation and replication efforts. The Template for Intervention Description and Replication (TIDieR) checklist, with guidance from the TIDieR-Telehealth extension, was used to describe NROL. The description was developed collaboratively by clinical academics, therapists, managers and researchers. Updated Consolidated Framework for Implementation Research domains were used to describe the context in which the innovation was delivered. NROL delivers online group-based real-time neurorehabilitation with technology assistance. It incorporates multidisciplinary targeted therapy and peer support to complement existing therapy. Procedures, materials and structure are detailed to demonstrate how NROL is embedded within a healthcare system. NROL uses existing NHS therapy workforce alongside dedicated NROL roles, including an essential technology support role. Selection of NROL groups is dependent on patient needs. The NROL innovation is tailored over time in response to feedback. NROL described here is successfully integrated within a regional stroke and neurorehabilitation network, aligns with local and national strategies and capitalises on an existing clinical-academic partnership. This comprehensive description of a regional NROL innovation, and clarification of core components, should facilitate other healthcare settings to adapt and implement NROL for their context. Continuous evaluation alongside implementation will ensure maximal impact for neurorehabilitation.

Impact of Visual Feedback Configurations in a Task-Oriented Immersive Virtual Reality Mirror Therapy.

Mirror Therapy is a form of upper limb therapy in stroke, which consists on showing the mirrored symmetric movement of the unimpaired side using a mirror placed in the medial sagittal plane. The illusion of movement corresponding to the impaired extremity could facilitate neuroplasticity, and assists patients in regaining certain lost motor functions. Recent studies have shown the potential and benefit of translating this effective therapy in an immersive virtual reality (VR) environment, using head mounted display and hand tracking systems. This work investigates the feasibility to use myoelectric control in an immersive VR environment for the mirror therapy to accomplish a funtional task. Surface electromyography sensors were used to measure muscle activation and detect user intention to perform grasping actions in two visual feedback configurations: unimanual and bimanual. Even though in both conditions the virtual mirrored hand is controlled by the healthy hand, the latter creates the illusion that two functional hands cooperate for grasping. A total of 18 healthy subjects participated in the evaluation of the environment, control method and a comparison between the two configurations. This includes self-rating surveys and performance metrics. Results from the Simulator Sickness Questionnaire showed negligible adverse symptoms related to the use of the VR-application proposed. Positive outcomes from the System Usability Scale and Presence Questionnaire affirmed its feasible as a form of therapy for the rehabilitation of hemiparetic stroke patients. Although the adapted Box and Blocks Test proved immediate training-induced learning, performance measures revealed no significant distinctions between visual feedback configurations.

Robotic exoskeleton-assisted walking rehabilitation for stroke patients: a bibliometric and visual analysis.

This study aimed to conduct a bibliometric analysis of the literature on exoskeleton robot assisted walking rehabilitation for stroke patients in the Web of Science Core Collection over the past decade. Retrieved literature on exoskeleton robot assisted gait training for stroke hemiplegic patients from the Web of Science Core Collection from 1 January 2014 to 31 January 2024. The search method was topic search, and the types of documents were "article, meeting abstract, review article, early access." CiteSpace was used to analyze the search results from countries, institutions, keywords, cited references and cited authors. A total of 1,349 articles were retrieved, and 1,034 were ultimately included for visualization analysis. The annual publication volume showed an upward trend, with countries, institutions, and authors from Europe and America in a leading position. The core literature was also published by authors from European and American countries. The keywords were divided into 8 clusters: # 0 soft robotic exit, # 1 robot assisted gain training, # 2 multiple scales, # 3 magnetic rheological brake, # 4 test retest reliability, # 5 electromechanical assisted training, # 6 cerebra salary, and # 7 slow gain. The early research direction focused on the development of exoskeleton robots, verifying their reliability and feasibility. Later, the focus was on the combination of exoskeleton robot with machine learning and other technologies, rehabilitation costs, and patient quality of life. This study provides a visual display of the research status, development trends, and research hotspots, which helps researchers in this field to grasp the research hotspots and choose future research directions.

An Adaptive Hybrid Brain Computer Interface for Hand Function Rehabilitation of Stroke Patients.

Motor imagery (MI) based brain computer interface (BCI) has been extensively studied to improve motor recovery for stroke patients by inducing neuroplasticity. However, due to the lower spatial resolution and signal-to-noise ratio (SNR) of electroencephalograph (EEG), MI based BCI system that involves decoding hand movements within the same limb remains lower classification accuracy and poorer practicality. To overcome the limitations, an adaptive hybrid BCI system combining MI and steady-state visually evoked potential (SSVEP) is developed to improve decoding accuracy while enhancing neural engagement. On the one hand, the SSVEP evoked by visual stimuli based on action-state flickering coding approach significantly improves the recognition accuracy compared to the pure MI based BCI. On the other hand, to reduce the impact of SSVEP on MI due to the dual-task interference effect, the event-related desynchronization (ERD) based neural engagement is monitored and employed for feedback in real-time to ensure the effective execution of MI tasks. Eight healthy subjects and six post-stroke patients were recruited to verify the effectiveness of the system. The results showed that the four-class gesture recognition accuracies of healthy individuals and patients could be improved to 94.37 ± 4.77% and 79.38 ± 6.26%, respectively. Moreover, the designed hybrid BCI could maintain the same degree of neural engagement as observed when subjects solely performed MI tasks. These phenomena demonstrated the interactivity and clinical utility of the developed system for the rehabilitation of hand function in stroke patients.

Relationship of Characteristics Adults Post-Stroke On Increasing Muscle Strength Rehabilitation In Bukittinggi City 2023

Introduction: Stroke is the third leading cause of death in the world. Long-term disability and paralysis becomes a burden for living stroke survivors. Disability results from muscle and nerve weakness upper and lower extremities. This research aims to identify the relationship between individual characteristics and history of stroke with muscle strength in stroke rehabilitation patients at home, Bukittinggi 2023. Materials and Methods: This research was conducted from May to June 2023. This study uses a cross-sectional descriptive analysis approach with questionnaires and Manual Muscle Testing (MMT). The total sample in this study was 42 respondents, using univariate and bivariate data. Data processing results using SPSS version 2.0 with the Qhi-Square Test. Results: based on gender showed that men were dominant at 71.4%, marital status at 69%, and in terms of education the majority was a bachelor's degree at 38.1%. self-employed jobs get 35.7% and socio-economic average 45.2% &gt; IDR 3,000,000., on muscle strength in post-stroke rehabilitation patients increased using the MMT method by 59.9%. Conclusion: there is no relationship between the characteristics of respondents (gender, marital status, education, employment and socio-economics) on increasing muscle strength in post-stroke rehabilitation patients at home with a p value greater than 0.05.

Current Study on Brain Computer Interface with Virtual Reality in Upper Limb Rehabilitation

Cerebral stroke has been a great issue in the world due to its high mortality rate and disability rate. Rehabilitation for stroke patients has been a hot research field. However, conventional therapies for disabled patients depend mainly on medication and rehabilitation therapist, which is a painful and time-consuming process. Besides, the rehabilitation process requires volitional movement, which is a challenge for severe stroke patients. Therefore, researchers came up with an approach to combine brain-computer interface (BCI) with traditional therapies. Through brain-computer interface technology, researchers collect brain signals to strengthen the motor pathway. Virtual reality (VR) is also a new technique that can be utilized in rehabilitation. This paper studied research from several teams. A research team came up with a hybrid VR-BCI system and designed a classification model based on neural network. Another research team introduced an experiment on a VR- BCI combined platform called REINVENT. The team assessed the feasibility and effect of the platform by analyzing data collected during the study. In addition, A VR game was developed for mild patients. Current studies on VR-BCI combined system have proved its feasibility. However, more clinical data and further research are also needed to assess its effect on rehabilitation.

Cognitive Function and Upper Limb Rehabilitation Training Post-Stroke Using a Digital Occupational Training System.

Stroke rehabilitation often requires frequent and intensive therapy to improve functional recovery. Virtual reality (VR) technology has shown the potential to meet these demands by providing engaging and motivating therapy options. The digital occupational training system is a VR application that utilizes cutting-edge technologies, including multi-touch screens, virtual reality, and human-computer interaction, to offer diverse training techniques for advanced cognitive capacity and hand-eye coordination abilities. The objective of this study was to assess the effectiveness of this program in enhancing cognitive function and upper extremity rehabilitation in stroke patients. The training and assessment consist of five cognitive modules covering perception, attention, memory, logical reasoning, and calculation, along with hand-eye coordination training. This research indicates that after eight weeks of training, the digital occupational training system can significantly improve cognitive function, daily living skills, attention, and self-care abilities in stroke patients. This software can be employed as a time-saving and clinically effective rehabilitation aid to complement traditional one-on-one occupational therapy sessions. In summary, the digital occupational training system shows promise and offers potential financial benefits as a tool to support the functional recovery of stroke patients.