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The purpose of this study was to investigate the effects of a combined robot-assisted gait training (RAGT) with standard physiotherapy (PT) on trunk control and posture in non-ambulatory children with cerebral palsy (CP). This nonrandomized, controlled study included 31 CP assigned into two groups. Study Group: RAGT (three times a week, 30 min/session, for 6 weeks) + PT. Control group: PT only. The patients were evaluated using gross motor function measure (GMFM)-88 (Section B, Sitting) and Trunk Impairment Scale (TIS), pre-treatment and 3rd month post-treatment. In the RAGT group, significant improvements were observed in the GMFM-B and TIS scores at the 3rd month post-treatment (p < 0.05). Comparison of the changes in GMFM-B and TIS scores from end to beginning of the study, the change in TIS static are significantly higher in the RAGT group than control group (p < 0.05). Addition of RAGT to standard physiotherapy seems to improve trunk control, sitting balance, and posture in non-ambulatory CP. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the effect of assistive robotic technologies on quality of life, functional independence, and perceived fatigue level in individuals with spinal cord injury (SCI). Material and Methods: This research involved a cohort of 25 patients who had been diagnosed with SCI. To assess their progress, clinical assessments were administered both at the commencement and completion of a six-week robotic rehabilitation treatment regimen. The evaluations encompassed the use of the Spinal Cord Independence Measure (SCIM III) to measure their performance in daily living activities and mobility. Additionally, the quality of life was assessed using the World Health Organization Quality of Life Scale - Short Form (WHOQOL-BREF) scale, while the levels of fatigue experienced during rehabilitation were gauged using the Modified Borg Scale (RPE). Results: The participants' average age and BMI were 40.72±1.28 kg/m2 and 23.43±0.57 year. Statistically significant differences were found in self-care (p=0.006) and mobility (p=0.004) values of SCIM III scale compared to pretreatment values. WHOQOL-BREF General health status, Physical health, Psychological, Social relations and Environment sub-parameters all showed statistically significant differences compared to pre-treatment values (p<0.001). There was a significant decrease in the RPE value to determine the level of fatigue during exertion in roboticwalking training (p<0.001). Conclusion: Assisted robotic rehabilitation approaches increased individual independence, quality of life and reduced fatigue during exertion in Individuals with SCI. We think that assisted robotic approaches applied in addition to traditional rehabilitation provide additional benefits in increasing the level of independence and quality of life of individuals with SCI in daily life and reducing fatigue during exertion. [ABSTRACT FROM AUTHOR]

Abstract Stroke-induced upper-extremity paralysis affects a substantial portion of the population, necessitating effective rehabilitation strategies. This study aimed to develop an automated program, incorporating the item response theory, for rehabilitation in patients with post-stroke upper-extremity paralysis, focusing on the ReoGo-J device, and to identify suitable robot parameters for robotic rehabilitation. ReoGo-J, a training device for upper-extremity disorders including 71 items, was administered to over 300 patients with varying degrees of post-stroke upper-extremity paralysis. Each item was rated on a three-point scale (0, very difficult; 1, adequate; 2, very easy). The results were analyzed using the graded response model, an extension of the two-parameter logistic model within the framework of the item response theory, to grade the training items based on ability of the patients. The relationship between the predicted ability, an indicator of the predicted ability the paralyzed upper-extremity to perform an item in the item response theory analysis (higher numbers indicate higher ability, lower numbers indicate lower ability), and the items in the Fugl–Meyer assessment (FMA), which indicate the degree of paralysis, was analyzed using Pearson’s correlation coefficient. This study included 312 patients with post-stroke upper-extremity paralysis. The predicted ability (θ) of the tasks included in the original ReoGo-J test for forward reaching, reaching for mouth, rotational reaching, radial reaching (2D), abduction reaching, reaching in eight directions, radial reach (3D), and reaching for head ranged from − 2.0 to − 0.8, − 1.3 to − 0.8, − 1.0 to − 0.1, − 0.7 to 0.3, − 0.2 to 0.4, − 0.4 to 0.6, − 0.1 to 0.6, and 0.5 to 0.6, respectively. Significantly high correlations (r = 0.80) were observed between the predicted ability of all patients and the upper-extremity items of shoulder-elbow-forearm in the FMA. We have introduced an automated program based on item response theory and determined the order of difficulty of the 71 training items in ReoGo-J. The strong correlation between the predicted ability and the shoulder-elbow-forearm items in FMA may be used to ameliorate post-stroke upper-extremity paralysis. Notably, the program allows for estimation of appropriate ReoGo-J tasks, enhancing clinical efficiency. Trial registration: https://www.umin.ac.jp/ctr/index-j.htm (UMIN000040127).

Motor impairment is a common issue in stroke patients, often affecting the upper limbs. To this standpoint, robotic neurorehabilitation has shown to be highly effective for motor function recovery. Notably, Machine learning (ML) may be a powerful technique able to identify the optimal kind and intensity of rehabilitation treatments to maximize the outcomes. This retrospective observational research aims to assess the efficacy of robotic devices in facilitating the functional rehabilitation of upper limbs in stroke patients through ML models. Specifically, clinical scales, such as the Fugl-Meyer Assessment (A-D) (FMA), the Frenchay Arm Test (FAT), and the Barthel Index (BI), were used to assess the patients' condition before and after robotic therapy. The values of these scales were predicted based on the patients' clinical and demographic data obtained before the treatment. The findings showed that ML models have high accuracy in predicting the FMA, FAT, and BI, with R-squared (R2) values of 0.79, 0.57, and 0.74, respectively. The findings of this study suggest that integrating ML into robotic therapy may have the capacity to establish a personalized and streamlined clinical practice, leading to significant improvements in patients' quality of life and the long-term sustainability of the healthcare system. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to examine the impact of robotic hand rehabilitation on hand function and quality of life in childrenwith cerebral palsy. Design: Children with cerebral palsy aged 7-16 yrs were divided into robotic rehabilitation (n = 9) or conventional rehabilitation (n = 10) groups for hand rehabilitation of 30 sessions. The primary outcomes were the Fugl-Meyer Assessment for Upper Extremity and Box and Block Test. The secondary outcomes were the Manual Ability Classification System, modified Ashworth scale, hand grasp and finger strengths, ABILHAND-Kids, Functional Independence Measure for Children, and PedsQL Quality of Life Inventory-CP Module. Results: In the robotic rehabilitation group, a significant improvement was found in all parameters after treatment (P < 0.05), except for the Functional Independence Measure (P = 0.081). In the conventional rehabilitation group, there was significant improvement after treatment in the modified Ashworth scale, Fugl-Meyer Assessment for Upper Extremity, hand grasp strength, Box and Block Test, ABILHAND-Kids, and PedsQL Quality of Life Inventory-CP Module (P < 0.05). Before and after treatment, all outcome parameters in the groups were similar (P > 0.05). Conclusions: Robotic hand rehabilitation is effective in improving motor function, manual dexterity, spasticity, and quality of life in children with cerebral palsy. However, it was not demonstrated to be superior to conventional rehabilitation. [ABSTRACT FROM AUTHOR]

Neurological diseases are observed in approximately 1 billion people worldwide. A further increase is foreseen at the global level as a result of population growth and aging. Individuals with neurological disorders often experience cognitive, motor, sensory, and lower extremity dysfunctions. Thus, the possibility of falling and balance problems arise due to the postural control deficiencies that occur as a result of the deterioration in the integration of multi-sensory information. We propose a novel rehabilitation framework, Integrated Balance Rehabilitation (I-BaR), to improve the effectiveness of the rehabilitation with objective assessment, individualized therapy, convenience with different disability levels and adoption of assist-as-needed paradigm and, with integrated rehabilitation process as whole, that is, ankle-foot preparation, balance, and stepping phases, respectively. Integrated Balance Rehabilitation allows patients to improve their balance ability by providing multi-modal feedback: visual via utilization of virtual reality; vestibular via anteroposterior and mediolateral perturbations with the robotic platform; proprioceptive via haptic feedback. [ABSTRACT FROM AUTHOR]

BACKGROUND: Rehabilitation assessment is a critical component of rehabilitation treatment. OBJECTIVE: This study focuses on a comprehensive analysis of patients' movement performance using the upper limb rehabilitation robot. It quantitatively assessed patients' motor control ability and constructed an intelligent grading model of functional impairments. These findings contribute to a deeper understanding of patients' motor ability and provide valuable insights for personalized rehabilitation interventions. METHODS: Patients at different Brunnstrom stages underwent rehabilitation training using the upper limb rehabilitation robot, and data on the distal movement positions of the patients' upper limbs were collected. A total of 22 assessment metrics related to movement efficiency, smoothness, and accuracy were extracted. The performance of these assessment metrics was measured using the Mann-Whitney U test and Pearson correlation analysis. Due to the issue of imbalanced sample categories, data augmentation was performed using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm based on weighted sampling, and an intelligent grading model of functional impairment based on the Extreme Gradient Boosting Tree (XGBoost) algorithm was constructed. RESULTS: Sixteen assessment metrics were screened. These metrics were effectively normalized to their maximum values, enabling the derivation of quantitative assessment scores for motor control ability across the three dimensions through a weighted fusion approach. Notably, when applied to the data-enhanced dataset, the intelligent grading model exhibited remarkable improvement, achieving an accuracy rate exceeding 0.98. Moreover, significant enhancements were observed in terms of precision, recall, and f1-score. CONCLUSION: The research findings demonstrate that this study enables the quantitative assessment of patients' motor control ability and intelligent grading of functional impairments, thereby contributing to the efficiency enhancement of clinical rehabilitation assessment. Moreover, this method resolves the issues associated with the subjectivity and prolonged periods of traditional rehabilitation assessment methods. [ABSTRACT FROM AUTHOR]

In this paper, an online adaptive super twisting sliding mode controller is proposed for a non-linear system. The adaptive controller has been designed in order to deal with the unknown dynamic uncertainties and give the best trajectory tracking. The adaptation is based on an optimal Particle Swarm Optimization (PSO) algorithm whose goal is online tuning the parameters through focusing on decreasing the objective function. The novelty of this study is online handling parameters setting in the conventional super twisting algorithm, bypass heavy offline calculation, and also avoid the instability and abrupt changing of the controller's parameters for better actuators lifetime. This novel approach has been applied on an upper limb exoskeleton robot for arm rehabilitation. Despite the changes of the dynamic model of the system which defers from one patient to another due to the direct interactions between the wearer and the exoskeleton, this control technique preserves its robustness with respect to bounded external disturbances. The effectiveness of the proposed adaptive controller has been proved in simulation and then in real-time experiment with two human subjects. A comparison between the proposed approach and classic super twisting algorithm has been conducted. The obtained results show the performance and efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

Loss of hand functions, often manifesting in the form of weakness or spasticity from conditions like stroke or multiple sclerosis, poses challenges in performing activities of daily living (ADLs). The broad area of rehabilitation robotics provides the tools and knowledge necessary for implementing efficient restorative therapies. These therapies aim to improve hand functionality with minimal therapist intervention. However, the human hand evolved for various precision and power gripping tasks, with its intricate anatomy featuring a large number of degrees of freedom—up to 31—which hinder its modeling in many rehabilitation scenarios. In the process of designing prosthetic devices, instrumented gloves, and rehabilitation devices, there is a clear need to obtain simplified rehabilitation-oriented hand models without compromising their representativeness across the population. This is where the concept of kinematic reduction, focusing on specific grasps, becomes essential. Thus, the objective of this study is to uncover the intra-finger dependencies during finger flexion/extension by analyzing a comprehensive database containing recorded trajectories for 23 different functional movements related to ADLs, involving 77 test subjects. The initial phase involves data wrangling, followed by correlation analysis aimed at selecting 116 dependency-movement relationships across all grasps. A regularized generalized linear model is then applied to select uncorrelated predictors, while a linear mixed-effect model, with reductions based on both predictor significance and effect size, is used for modeling the dependencies. As a final step, agglomerative clustering of models is performed to further facilitate flexibility in tradeoffs in hand model accuracy/reduction, allowing the modeling of finger flexion extensions using 5–15 degrees of freedom only. [ABSTRACT FROM AUTHOR]

IntroductionMost patients suffering from neurological disorders endure varying degrees of upper limb dysfunction, limiting their everyday activities, with only a limited number regaining full arm use. Robotic and technological rehabilitation has been demonstrated to be a feasible solution to guarantee an effective rehabilitation to recover upper limb performance or to prevent complications of upper limb immobility. However, there is currently a lack of studies which analyze the sustainability of robotic and technological rehabilitation by comparing its costs to conventional rehabilitation pathways.MethodsSince technology-based and conventional rehabilitation of the upper limb have been demonstrated to have comparable efficacy when the rehabilitation dose is matched, our study concentrates on a cost minimization analysis. The aim of the study is to compare the costs of a “mixed” rehabilitation cycle, which combines conventional and technology-based treatments (the latter delivered with a single therapist supervising several patients), with a cycle of purely conventional treatments. This has been done by developing a cost model and retrospectively analyzing the costs sustained by an Italian hospital which has adopted such a mixed model. A sensitivity analysis has been done to identify the parameters of the model that have the greatest influence on cost difference and to evaluate their optimal values in terms of efficiency of mixed rehabilitation. Finally, probabilistic simulations have been applied to consider the variability of model parameters around such optimized values and evaluate the probability of achieving a given level of savings.ResultsWe found a cost difference of 49.60 € per cycle in favor of mixed rehabilitation. The sensitivity analysis demonstrated that, in the situation of the hospital under investigation, the parameter having the largest influence on the cost difference is the number of robotic treatments in a mixed rehab cycle. Probabilistic simulations indicate a probability higher than 98% of an optimized mixed rehabilitation cycle being less expensive than a pure conventional one.ConclusionThrough a retrospective cost analysis, we found that the technology-based mixed rehabilitation approach, within a specific organizational model allowing a single physiotherapist to supervise up to four patients concurrently, allowed cost savings compared to the conventional rehabilitation model.

The use of social assistive robots for interactive stimulation has strong potential in neurorehabilitation therapies. It is of particular interest in the case of pediatric patients to promote children's motivation and adherence, specially when those robots are able of guide gamified activities, as it is the case of NAOTherapist. NAOTherapist is a Social Assistive Robotics (SAR) platform for hands-off rehabilitation based on upper-limb activities, that was originally designed for pediatric patients with Cerebral Palsy (CP) or Obstetric Braxial Plexus Palsy (OBPP). Formerly, it endowed the therapists with tools to perform rehabilitation exercises. This paper proposes the gamification of NAOTherapist in order to incorporate additional characteristics which allow its intensive use in new rehabilitation procedures, such as the Hand-Arm Bimanual Intensive Therapy (HABIT). This intensive therapy setting involves daily activities in several consecutive days, which require a strong engagement of the patients with the therapeutic methods and the acceptation of the NAOTherapist as a rehabilitation system. The gamified system shows very accurate results considering the different aspects defined in the USUS methodology; namely Usability, Social acceptance, User experience and Societal impact. [ABSTRACT FROM AUTHOR]

This systematic review examines groundbreaking advancements in hand rehabilitation, driven by significant progress in medical technology. Adhering to PRISMA guidelines, the review surveyed articles from various databases, emphasizing the utilization of advanced technologies for hand rehabilitation across diverse conditions such as fractures, burns, amputations, and postoperative care. Out of the 1562 studies analyzed, ten articles that met the inclusion criteria were identified. Key technologies like IMES (Implantable Myoelectric Sensor) technology are scrutinized for their potential to transform prosthetic control, providing intuitive functionality and substantial physiological hand function for individuals with limb loss. Similarly, advancements in orthotic devices like the VacoHand Orthosis are showcased for their improvements in stabilizing and aiding the healing process in wrist immobilization and rehabilitation. The integration of virtual reality (VR) emerges as a pivotal convergence point, offering supplementary therapy to enhance directionspecific improvements in wrist and forearm mobility, hand function, and work-related tasks. Additional advantages include program customization, heightened engagement through interactive approaches, and improved functional outcomes such as hand strength and range of motion. While showcasing transformative potential, the review acknowledges the need for further research through larger sample sizes, standardized methodologies and more in-depth investigations to optimize these technologies. Overall, this review underlines the remarkable advancements achieved in hand rehabilitation through technological innovations, offering hope and improved outcomes for individuals grappling with various hand-related challenges. [ABSTRACT FROM AUTHOR]

Neurological diseases are observed in approximately 1 billion people worldwide. A further increase is foreseen at the global level as a result of population growth and aging. Individuals with neurological disorders often experience cognitive, motor, sensory, and lower extremity dysfunctions. Thus, the possibility of falling and balance problems arise due to the postural control deficiencies that occur as a result of the deterioration in the integration of multi-sensory information. We propose a novel rehabilitation framework, Integrated Balance Rehabilitation (I-BaR), to improve the effectiveness of the rehabilitation with objective assessment, individualized therapy, convenience with different disability levels and adoption of assist-as-needed paradigm and, with integrated rehabilitation process as whole, that is, ankle-foot preparation, balance, and stepping phases, respectively. Integrated Balance Rehabilitation allows patients to improve their balance ability by providing multi-modal feedback: visual via utilization of virtual reality; vestibular via anteroposterior and mediolateral perturbations with the robotic platform; proprioceptive via haptic feedback.

Transcutaneous spinal stimulation (TSS) is a promising rehabilitative intervention to restore motor function and coordination for individuals with spinal cord injury (SCI). The effects of TSS are most commonly assessed by evaluating muscle response to stimulation using surface electromyography (sEMG). Given the increasing use of robotic devices to deliver therapy and the emerging potential of hybrid rehabilitation interventions that combine neuromodulation with robotic devices, there is an opportunity to leverage the on-board sensors of the robots to measure kinematic and torque changes of joints in the presence of stimulation. This paper explores the potential for robotic assessment of the effects of TSS delivered to the cervical spinal cord. We used a four degree-of-freedom exoskeleton to measure the torque response of upper limb (UL) joints during stimulation, while simultaneously recording sEMG. We analyzed joint torque and electromyography data generated during TSS delivered over individual sites of the cervical spinal cord in neurologically intact participants. We show that site-specific effects of TSS are manifested not only by modulation of the amplitude of spinally evoked motor potentials in UL muscles, but also by changes in torque generated by individual UL joints. We observed preferential resultant action of proximal muscles and joints with stimulation at the rostral site, and of proximal joints with rostral-lateral stimulation. Robotic assessment can be used to measure the effects of TSS, and could be integrated into complex control algorithms that govern the behavior of hybrid neuromodulation-robotic systems.

Individuals with neurological disorders often exhibit altered manual dexterity and muscle weakness in their upper limbs. These motor impairments with tremor lead to severe difficulties in performing Activities of Daily Living (ADL). There is a critical need for ADL-focused robotic training that improves individual’s strength when engaging with dexterous ADL tasks. This research introduces a new approach to training ADLs by employing a novel robotic rehabilitation system, Spherical Parallel INstrument for Daily Living Emulation (SPINDLE), which incorporates Virtual Reality (VR) to simulate ADL tasks. The study results present the feasibility of training individuals with movements similar to ADLs while interacting with the SPINDLE. A new game-based robotic training paradigm is suggested to perform ADL tasks at various intensity levels of resistance as needed. The proposed system can facilitate the training of various ADLs requiring 3-dimensional rotational movements by providing optimal resistance and visual feedback. We envision this system can be utilized as a table-top home device by restoring the impaired motor function of individuals with tremor and muscle weakness, guiding to improved ADL performance and quality of life.

In this paper, we develop a supportive device to monitor the rehabilitation exercise process for people with disabilities to use their hands. The prototype is designed to include a resistance cable system to support the rehabilitation of the patient's hand motor function. During training, the patient pulls the resistance cable to exercise, and the resistance cable system applies force to the product's sensor block. The sensor block is capable of measuring parameters when force is applied, allowing the calculation of hand force during the patient's training. The hand force data obtained from the sensor block is then sent to a separate IoT-based application. This mobile-platform app is particularly developed for hosting data and providing a view for user to track his/her process of training. Additionally, the product is designed with angle measurement capabilities to track motion coordinates during the patient's hand training. The product is tailored to the physique of Vietnamese individuals. The patient's training results are stored on the Internet and displayed on a mobile application, facilitating the monitoring of training progress, comparing patient training results with standard data, and allowing remote monitoring by the patient's family members or healthcare professionals.

• Exoskeleton ExoAthlet showed more positive impact on subparameters of quality of life. • Inclusion of robotic gait aids in current rehabilitation can provide advantages. • Both gait training approaches positively affect quality of life in stroke patients. • Functional independence improves in both gait training methods in stroke patients. • The functional capacity increases in both gait training approaches in stroke patients. Even 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. The 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. Results 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). This 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. [ABSTRACT FROM AUTHOR]

The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost–benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology. [ABSTRACT FROM AUTHOR]

Effective control of rehabilitation robots is of paramount importance and requires increased attention to achieve a fully reliable, automated system for practical applications. As the domain of robotic rehabilitation progresses rapidly, the imperative for precise and dependable control mechanisms grows. In this study, we present an innovative control scheme integrating state-of-the-art machine learning algorithms with traditional control techniques. Our approach offers enhanced adaptability to patient-specific needs while ensuring safety and effectiveness. We introduce a model-free feedback linearization control method underpinned by deep neural networks and online observation. While our controller is model-free, and system dynamics are learned during training phases, we employ an online observer to robustly estimate uncertainties that the systems may face in real-time, beyond their training. The proposed technique was tested through different simulations with varying initial conditions and step references, demonstrating the controller's robustness and adaptability. These simulations, combined with Lyapunov's stability verification, validate the efficacy of our proposed scheme in effectively controlling the system under diverse conditions. [ABSTRACT FROM AUTHOR]

Copyright of Istanbul Gelisim University Journal of Health Sciences / İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi is the property of Istanbul Gelisim Universitesi Saglik Bilimleri Yuksekokulu 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.)

Abstract Background Learning of a visuomotor task not only leads to changes in motor performance but also improves proprioceptive function of the trained joint/limb system. Such sensorimotor learning may show intra-joint transfer that is observable at a previously untrained degrees of freedom of the trained joint. Objective Here, we examined if and to what extent such learning transfers to neighboring joints of the same limb and whether such transfer is observable in the motor as well as in the proprioceptive domain. Documenting such intra-limb transfer of sensorimotor learning holds promise for the neurorehabilitation of an impaired joint by training the neighboring joints. Methods Using a robotic exoskeleton, 15 healthy young adults (18–35 years) underwent a visuomotor training that required them to make continuous, increasingly precise, small amplitude wrist movements. Wrist and elbow position sense just-noticeable‐difference (JND) thresholds and spatial movement accuracy error (MAE) at wrist and elbow in an untrained pointing task were assessed before and immediately after, as well as 24 h after training. Results First, all participants showed evidence of proprioceptive and motor learning in both trained and untrained joints. The mean JND threshold decreased significantly by 30% in trained wrist (M: 1.26° to 0.88°) and by 35% in untrained elbow (M: 1.96° to 1.28°). Second, mean MAE in untrained pointing task reduced by 20% in trained wrist and the untrained elbow. Third, after 24 h the gains in proprioceptive learning persisted at both joints, while transferred motor learning gains had decayed to such extent that they were no longer significant at the group level. Conclusion Our findings document that a one-time sensorimotor training induces rapid learning gains in proprioceptive acuity and untrained sensorimotor performance at the practiced joint. Importantly, these gains transfer almost fully to the neighboring, proximal joint/limb system.

Motor impairment is a common issue in stroke patients, often affecting the upper limbs. To this standpoint, robotic neurorehabilitation has shown to be highly effective for motor function recovery. Notably, Machine learning (ML) may be a powerful technique able to identify the optimal kind and intensity of rehabilitation treatments to maximize the outcomes. This retrospective observational research aims to assess the efficacy of robotic devices in facilitating the functional rehabilitation of upper limbs in stroke patients through ML models. Specifically, clinical scales, such as the Fugl-Meyer Assessment (A-D) (FMA), the Frenchay Arm Test (FAT), and the Barthel Index (BI), were used to assess the patients’ condition before and after robotic therapy. The values of these scales were predicted based on the patients’ clinical and demographic data obtained before the treatment. The findings showed that ML models have high accuracy in predicting the FMA, FAT, and BI, with R-squared (R2) values of 0.79, 0.57, and 0.74, respectively. The findings of this study suggest that integrating ML into robotic therapy may have the capacity to establish a personalized and streamlined clinical practice, leading to significant improvements in patients’ quality of life and the long-term sustainability of the healthcare system.

Background: Mood disorders, especially depression, and emotional difficulties such as anxiety are very common problems among patients with spinal cord injuries (SCIs). The lack of physical training may deteriorate their mental state, which, in turn, has a significant impact on their improvement in functioning. The aim of the present study was to examine the influence of innovative rehabilitation approaches involving robotic-assisted gait therapy (RAGT) on the depression and anxiety symptoms in patients with SCI. Methods: A total of 110 participants with subacute SCIs were enrolled in this single-center, single-blinded, single-arm, prospective study; patients were divided into experimental (robotic-assisted gait therapy (RAGT)) and control (conventional gait therapy with dynamic parapodium (DPT)) groups. They received five training sessions per week over 7 weeks. At the beginning and end of therapy, the severity of depression was assessed via the Depression Assessment Questionnaire (KPD), and that of anxiety symptoms was assessed via the State–Trait Anxiety Inventory (STAI X-1). Results: SCI patients in both groups experienced significantly lower levels of anxiety- and depression-related symptoms after completing the seven-week rehabilitation program (KPD: Z = 6.35, p < 0.001, r = 0.43; STAI X-1: Z = −6.20, p < 0.001, r = 0.42). In the RAGT group, post-rehabilitation measurements also indicated an improvement in psychological functioning (i.e., decreases in depression and anxiety and an increase in self-regulation (SR)). Significant results were noted for each variable (STAI X-1: Z = −4.93; KPD: Z = −5.26; SR: Z = −3.21). In the control group, there were also decreases in the effects on depression and state anxiety and an increase in self-regulation ability (STAI X-1: Z = −4.01; KPD: Z = −3.65; SR: Z = −2.83). The rehabilitation modality did not appear to have a statistically significant relationship with the magnitude of improvement in the Depression Assessment Questionnaire (KPD) (including self-regulation) and State–Trait Anxiety Inventory (STAI) scores. However, there were some significant differences when comparing the groups by the extent and depth of the injury and type of paralysis. Moreover, the study did not find any significant relationships between improvements in physical aspects and changes in psychological factors. Conclusions: Subjects in the robotic-assisted gait therapy (RAGD) and dynamic parapodium training (DPT) groups experienced decreases in anxiety and depression after a 7-week rehabilitation program. However, the rehabilitation modality (DPT vs. RAGT) did not differentiate between the patients with spinal cord injuries in terms of the magnitude of this change. Our results suggest that individuals with severe neurological conditions and complete spinal cord injuries (AIS A, according to the Abbreviated Injury Scale classification) may experience greater benefits in terms of changes in the psychological parameters after rehabilitation with RAGT. [ABSTRACT FROM AUTHOR]

Adolescent Idiopathic Scoliosis (AIS) may affect the quality of the patient’s life if it is left untreated. Bracing is prescribed to halt or reduce the curvature progression and avoid surgery. However, the in-brace correction pressure remains unclear, and it is controlled passively by tightening/losing the brace’s strap. Computational modeling has recently attracted researchers’ attention to predict and optimize the AIS bracing treatment. In this paper, a Multi Body-Finite Element (MB-FE) Simscape model and an analytical model of the AIS bracing treatment are created. The MB-FE Simscape model is used to predict the in-brace correction pressure. Furthermore, a Novel Position-based Impedance Control (NPIC) is proposed to control the dynamic interaction between the robotic brace and torso. In this method, the error between the desired and estimated impedance parameters is involved in the controller design to improve the performance of the typical PIC in terms of pose tracking and impedance model tracking. In-vivo data from the literature and numerical simulations are used to validate the MB-FE Simscape model and analytical model. The performance of the proposed controller is verified using numerical simulations in terms of pose tracking and impedance model tracking. [ABSTRACT FROM AUTHOR]

Background: Learning of a visuomotor task not only leads to changes in motor performance but also improves proprioceptive function of the trained joint/limb system. Such sensorimotor learning may show intra-joint transfer that is observable at a previously untrained degrees of freedom of the trained joint. Objective: Here, we examined if and to what extent such learning transfers to neighboring joints of the same limb and whether such transfer is observable in the motor as well as in the proprioceptive domain. Documenting such intra-limb transfer of sensorimotor learning holds promise for the neurorehabilitation of an impaired joint by training the neighboring joints. Methods: Using a robotic exoskeleton, 15 healthy young adults (18–35 years) underwent a visuomotor training that required them to make continuous, increasingly precise, small amplitude wrist movements. Wrist and elbow position sense just-noticeable‐difference (JND) thresholds and spatial movement accuracy error (MAE) at wrist and elbow in an untrained pointing task were assessed before and immediately after, as well as 24 h after training. Results: First, all participants showed evidence of proprioceptive and motor learning in both trained and untrained joints. The mean JND threshold decreased significantly by 30% in trained wrist (M: 1.26° to 0.88°) and by 35% in untrained elbow (M: 1.96° to 1.28°). Second, mean MAE in untrained pointing task reduced by 20% in trained wrist and the untrained elbow. Third, after 24 h the gains in proprioceptive learning persisted at both joints, while transferred motor learning gains had decayed to such extent that they were no longer significant at the group level. Conclusion: Our findings document that a one-time sensorimotor training induces rapid learning gains in proprioceptive acuity and untrained sensorimotor performance at the practiced joint. Importantly, these gains transfer almost fully to the neighboring, proximal joint/limb system. [ABSTRACT FROM AUTHOR]

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Studies have reported inconclusive results regarding the effectiveness and clinical indications of the exclusive use of human–robotic interactive gait training (HIT) in patients with post-stroke dementia (PSD). This study aimed to compare the effects of human–robotic interactive gait training (HIT) and conventional physiotherapy (CPT) on cognitive and sensorimotor functions, trunk balance and coordination, dynamic and static balance, and activities related to daily living performance in patients with PSD. Forty-eight patients with PSD who received 60-minute therapy sessions three times per week for 6 weeks were assigned to either the CPT (n = 25) or HIT (n = 23) group. The clinical outcomes included the scores of the mini-mental state examination (MMSE), Fugl–Meyer assessment (FMA), trunk impairment scale (TIS), Berg balance scale (BBS), and modified Barthel index (MBI). Friedman tests were conducted at p < 0.05. The Friedman tests showed that HIT had superior effects to CPT in relation to MMSE, FMA, and TIS (p < 0.05), but not in relation to BBS and MBI (p > 0.05). Our results provide promising clinical evidence that HIT significantly improves cognitive and sensorimotor recovery functions, as well as trunk balance and coordination, in patients with PSD who cannot concurrently perform dual cognitive–locomotor tasks. [ABSTRACT FROM AUTHOR]

Loss of hand functions, often manifesting in the form of weakness or spasticity from conditions like stroke or multiple sclerosis, poses challenges in performing activities of daily living (ADLs). The broad area of rehabilitation robotics provides the tools and knowledge necessary for implementing efficient restorative therapies. These therapies aim to improve hand functionality with minimal therapist intervention. However, the human hand evolved for various precision and power gripping tasks, with its intricate anatomy featuring a large number of degrees of freedom—up to 31—which hinder its modeling in many rehabilitation scenarios. In the process of designing prosthetic devices, instrumented gloves, and rehabilitation devices, there is a clear need to obtain simplified rehabilitation-oriented hand models without compromising their representativeness across the population. This is where the concept of kinematic reduction, focusing on specific grasps, becomes essential. Thus, the objective of this study is to uncover the intra-finger dependencies during finger flexion/extension by analyzing a comprehensive database containing recorded trajectories for 23 different functional movements related to ADLs, involving 77 test subjects. The initial phase involves data wrangling, followed by correlation analysis aimed at selecting 116 dependency-movement relationships across all grasps. A regularized generalized linear model is then applied to select uncorrelated predictors, while a linear mixed-effect model, with reductions based on both predictor significance and effect size, is used for modeling the dependencies. As a final step, agglomerative clustering of models is performed to further facilitate flexibility in tradeoffs in hand model accuracy/reduction, allowing the modeling of finger flexion extensions using 5–15 degrees of freedom only.

Background: Recent studies have investigated the effects of exercise on the functional capacity of older adults; training with a balance exercise assist robot (BEAR) effectively improves posture. This study compared the clinical safety and efficacy of training using BEAR video games to conventional resistance training in older adults with cardiovascular disease (CVD). Methods: Ninety patients (mean age: 78 years) hospitalized due to worsening CVD were randomized to cardiac rehabilitation (CR) Group R (conventional resistance training) or Group B (training using BEAR). After appropriate therapy, patients underwent laboratory testing and functional evaluation using the timed up-and-go test (TUG), short physical performance battery (SPPB), and functional independence measure (FIM) just before discharge and 4 months after CR. The rates of CVD readmission, cardiac death, and fall-related fractures were monitored. Results: BEAR had no adverse effects during exercise. At 4 months, TUG and SPPB improved significantly in both groups, with no significant difference between them. FIM motor and the Geriatric Nutritional Risk Index were significantly improved in Group B versus Group R. There was no significant difference in cardiac events and fall-related fractures between the two groups. Conclusion: CR with BEAR is safe and comparable to conventional resistance training for improving balance in older adults with CVD.

This systematic review examines groundbreaking advancements in hand rehabilitation, driven by significant progress in medical technology. Adhering to PRISMA guidelines, the review surveyed articles from various databases, emphasizing the utilization of advanced technologies for hand rehabilitation across diverse conditions such as fractures, burns, amputations, and postoperative care. Out of the 1562 studies analyzed, ten articles that met the inclusion criteria were identified. Key technologies like IMES (Implantable Myoelectric Sensor) technology are scrutinized for their potential to transform prosthetic control, providing intuitive functionality and substantial physiological hand function for individuals with limb loss. Similarly, ad-vancements in orthotic devices like the VacoHand Orthosis are showcased for their improve-ments in stabilizing and aiding the healing process in wrist immobilization and rehabilitation. The integration of virtual reality (VR) emerges as a pivotal convergence point, offering sup-plementary therapy to enhance direction-specific improvements in wrist and forearm mobility, hand function, and work-related tasks. Additional advantages include program customization, heightened engagement through interactive approaches, and improved functional outcomes such as hand strength and range of motion. While showcasing transformative potential, the re-view acknowledges the need for further research through larger sample sizes, standardized methodologies and more in-depth investigations to optimize these technologies. Overall, this review underlines the remarkable advancements achieved in hand rehabilitation through technological innovations, offering hope and improved outcomes for individuals grappling with various hand-related challenges.

Background: Trunk stabilization, which is a factor that directly affects the performance of affected upper-limb movements in stroke patients, is of critical importance in the performance of selective motor control. Aims: This study aimed to investigate the effects on upper-limb motor function of the addition of robotic rehabilitation (RR) and conventional rehabilitation (CR) to intensive trunk rehabilitation (ITR). Methods: A total of 41 subacute stroke patients were randomly allocated to two groups: RR and CR. Both groups received the same ITR procedure. Following ITR, a robot-assisted rehabilitation program of 60 min, 5 days a week, for 6 weeks, was applied to the RR group, and an individualized upper-limb rehabilitation to the CR group. Assessments were made at baseline and after 6 weeks using the Trunk Impairment Scale (TIS), Fugl-Meyer Upper Extremity Motor Evaluation Scale (FMA-UE), and Wolf Motor Function Test (WMFT). Results: Improvements were obtained in the TIS, FMA-UE, and WMFT scores for both groups (p < 0.001), with no superiority detected between the groups (p > 0.05). The RR group scores were relatively high, but not to a statistically significant. Conclusions: When added to intensive trunk rehabilitation, the robot-assisted systems, which are recommended as a stand-alone therapy method, produced similar results to conventional therapies. This technology can be used as an alternative to conventional methods under appropriate conditions of clinical opportunity, access, time management, and staff limitations. However, when RR is combined with traditional interventions such as intensive trunk rehabilitation, it is essential to investigate if the real effect is due to the robotic rehabilitation or the accumulation of positive effects of excessive movement or force spread associated with trained muscles. Registration: This trial was retrospectively registered in the ClinicalTrials.gov with NCT05559385 registration number (25/09/2022). [ABSTRACT FROM AUTHOR]

Background and Objectives: Although postoperative C5 palsy is a frequent complication of cervical spine surgery, no effective therapeutic rehabilitation approach has been established for postoperative C5 palsy. The purpose of this study was to find evidence confirming the effectiveness and feasibility of robotic Hybrid Assistive Limb (HAL) shoulder exercises for C5 palsy. Materials and Methods: In this before-after, uncontrolled case series clinical study, we performed a mean of 11.7 shoulder training sessions using a shoulder HAL immediately after the onset of C5 palsy in seven shoulders of six patients who developed postoperative C5 palsy and had difficulty raising their shoulder during the acute postoperative phase of cervical spine surgery. Shoulder HAL training was introduced as early as possible after evaluating the general condition of all inpatients who developed C5 palsy. Patients underwent shoulder abduction training using shoulder HAL on an inpatient and outpatient basis at 2-week or 1-month intervals. Adverse events associated with shoulder HAL training were investigated. The shoulder abduction angle and power without the shoulder HAL were evaluated before shoulder HAL usage, at every subsequent session, and upon completion of all sessions. Results: Severe adverse events due to shoulder HAL training were not reported. After completion of all shoulder HAL sessions, all patients showed improved shoulder elevation, while shoulder abduction angle and power improved over time. Conclusions: Shoulder elevation training with HAL in patients in the acute stage of postoperative C5 palsy has the potential to demonstrate improvement in shoulder joint function with a low risk of developing severe adverse events. [ABSTRACT FROM AUTHOR]

The aim of our study was to evaluate the effect of robot-assisted walking in different modalities on cardiorespiratory responses and energy consumption in subacute stroke patients. Our study consisted of 16 individuals between the ages of 18–65 years. Individuals diagnosed with hemiplegia after unilateral ischemic or haemorrhagic stroke constitute the stroke group. Eight subacute stroke individuals were included in the experimental group, and eight healthy individuals were included in the control group. Each participant tested on the Lokomat in three consecutive days in random sequence, with three tests: the first test: 100% guiding strength (GF)and 100% body weight support (BWS); the second test 80% GF, 50% BWS; the third test 60% GF, 30% BWS was achieved. Gas analyzer (Cosmed, Quark CPET, Italy) measurements were made with the help of a mask to evaluate the cardiorespiratory responses of the participants during all tests. In the comparison of the three test results of the two groups separately, the stroke group's oxygen consumption (VO2), carbon dioxide production (VCO2), tidal volume (VT), pulse reserve (HRR), calories burned per hour (EEh), Borg dyspnea values, control group's VO2, VCO2, VE, HR, HRR, and EEh, Borg values were statistically significantly different (p < 0.005). It was seen that the third test results were significantly greater than the first and second test results (p < 0.005). By decreasing GF and BWS values during robot-assisted walking, adequate cardio-metabolic and energy response in both subacute stroke patients and healthy individuals could be achieved. These results show us that it is important to consider the cardiorespiratory function of the patient when choosing training protocols. [ABSTRACT FROM AUTHOR]

Soft-robotics for biomedical applications, such as rehabilitation robots, is a field of intense research activity. Different actuation solutions have been proposed in the last decades, involving study and development of soft actuators of different types and materials. The purpose of the paper is to present procedures for an optimized design, and for easy and low cost production and characterization of monolithic PneuNets soft-actuators. An innovative design approach has been developed. The parameterization of the geometry, combined with FEM simulations is the basis for an optimized design of the actuator, as a function of the obtained bending and of the generated forces. Simple and cheap characterization setup and procedures have been identified for the actuator characterization and for simulation results validation. An easy and low-cost fabrication method based on lost wax core obtained through a silicone based mold has been developed for a monolithic PneuNets soft-actuator. The proposed solution performs well in bending, without the need for a strain limiting layer. Experimental results validated simulations, confirming the feasibility of adopting an optimized simulation-based design approach. [ABSTRACT FROM AUTHOR]