Innovative Metaheuristic Optimization Approach with a Bi-Triad for Rehabilitation Exoskeletons.

The present work proposes a comprehensive metaheuristic methodology for the development of a medical robot for the upper limb rehabilitation, which includes the topological optimization of the device, kinematic models (5 DOF), human-robot interface, control and experimental tests. This methodology applies two cutting-edge triads: (1) the three points of view in engineering design (client, designer and community) and (2) the triad formed by three pillars of Industry 4.0 (autonomous machines and systems, additive manufacturing and simulation of virtual environments). By applying the proposed procedure, a robotic mechanism was obtained with a reduction of more than 40% of its initial weight and a human-robot interface with three modes of operation and a biomechanically viable kinematic model for humans. The digital twin instance and its evaluation through therapeutic routines with and without disturbances was assessed; the average RMSEs obtained were 0.08 rad and 0.11 rad, respectively. The proposed methodology is applicable to any medical robot, providing a versatile and effective solution for optimizing the design and development of healthcare devices. It adopts an innovative and scalable approach to enhance their processes.

A State-of-the-Art of Exoskeletons in Line with the WHO's Vision on Healthy Aging: From Rehabilitation of Intrinsic Capacities to Augmentation of Functional Abilities.

The global aging population faces significant health challenges, including an increasing vulnerability to disability due to natural aging processes. Wearable lower limb exoskeletons (LLEs) have emerged as a promising solution to enhance physical function in older individuals. This systematic review synthesizes the use of LLEs in alignment with the WHO's healthy aging vision, examining their impact on intrinsic capacities and functional abilities. We conducted a comprehensive literature search in six databases, yielding 36 relevant articles covering older adults (65+) with various health conditions, including sarcopenia, stroke, Parkinson's Disease, osteoarthritis, and more. The interventions, spanning one to forty sessions, utilized a range of LLE technologies such as Ekso®, HAL®, Stride Management Assist®, Honda Walking Assist®, Lokomat®, Walkbot®, Healbot®, Keeogo Rehab®, EX1®, overground wearable exoskeletons, Eksoband®, powered ankle-foot orthoses, HAL® lumbar type, Human Body Posturizer®, Gait Enhancing and Motivation System®, soft robotic suits, and active pelvis orthoses. The findings revealed substantial positive outcomes across diverse health conditions. LLE training led to improvements in key performance indicators, such as the 10 Meter Walk Test, Five Times Sit-to-Stand test, Timed Up and Go test, and more. Additionally, enhancements were observed in gait quality, joint mobility, muscle strength, and balance. These improvements were accompanied by reductions in sedentary behavior, pain perception, muscle exertion, and metabolic cost while walking. While longer intervention durations can aid in the rehabilitation of intrinsic capacities, even the instantaneous augmentation of functional abilities can be observed in a single session. In summary, this review demonstrates consistent and significant enhancements in critical parameters across a broad spectrum of health conditions following LLE interventions in older adults. These findings underscore the potential of LLE in promoting healthy aging and enhancing the well-being of older adults.

Wearing a back-support exoskeleton alters lower-limb joint kinetics during single-step recovery following a forward loss of balance.

We assessed the effects of a passive, back-support exoskeleton (BSE) on lower-limb joint kinetics during the initiation and swing phases of recovery from a forward loss of balance. Sixteen (8M, 8F) young, healthy participants were released from static forward-leaning postures and attempted to recover their balance with a single-step while wearing a BSE (backXTM) with different levels of support torque and in a control condition. The BSE provided âˆ¼ 15-20 Nm of external hip extension torque on the stepping leg at the end of initiation and beginning of swing phases. Participants were unable to generate sufficient hip flexion torque, power, and work to counteract this external torque, although they sustained hip flexion torque for a more prolonged period, resulting in slightly increased hip contribution to positive leg work (compared to control). However, net positive leg work, and the net contribution of hip joint (human + BSE) to total leg work decreased with BSE use. While all participants had changes in hip joint kinetics, a significant compensatory increase in ankle contribution to positive leg work was observed only among females. Our results suggest that BSE use adversely affects reactive stepping by decreasing the stepping leg kinetic energy for forward propulsion, and that the relative contributions of lower-limb joints to total mechanical work done during balance recovery are altered by BSE use. BSEs may thus need to be implemented with caution for dynamic tasks in occupational settings, as they may impair balance recovery following a forward loss of balance.

Bilateral tibial fractures associated with powered exoskeleton use in complete spinal cord injury - a case report & literature review.

INTRODUCTION: Powered robotic exoskeleton (PRE) physiotherapy programmes are a relatively novel frontier which allow patients with reduced mobility to engage in supported walking. Research is ongoing regarding their utility, risks, and benefits. This article describes the case of two fractures occurring in one patient using a PRE. CASE: We report the case of a 54 year old man who sustained bilateral tibial fractures while using a PRE, on a background of T10 AIS A SCI. The initial session was discontinued due to acute severe bilateral knee swelling after approximately 15 min. The patient attended their local hospital the following day, where radiographs demonstrated bilateral proximal tibial fractures. The patient was treated with manipulation under anaesthetic and long-leg casting for five weeks, at which point he was stepped down to hinged knee braces which were weaned gradually while he remained non-weight bearing for 12 weeks. The patient was investigated with DEXA scan and was diagnosed with osteoporosis. He was liaised with rheumatology services and bone protection was initiated. Fracture healing was achieved and weight-bearing precautions were discontinued, however this period of immobilisation led to significant spasticity. The patient was discharged from orthopaedic services, with ongoing rehabilitation and physiotherapy follow-up. CONCLUSION: PRE assisted physiotherapy programmes are a promising concept in terms of rehabilitation and independence, however they are not without risk and it is important that both providers and patients are aware of this. Furthermore, SCI patients are at increased risk for osteoporosis and should be monitored and considered for bone protection.

A six degrees-of-freedom cable-driven robotic platform for head-neck movement.

This paper introduces a novel cable-driven robotic platform that enables six degrees-of-freedom (DoF) natural head-neck movements. Poor postural control of the head-neck can be a debilitating symptom of neurological disorders such as amyotrophic lateral sclerosis and cerebral palsy. Current treatments using static neck collars are inadequate, and there is a need to develop new devices to empower movements and facilitate physical rehabilitation of the head-neck. State-of-the-art neck exoskeletons using lower DoF mechanisms with rigid linkages are limited by their hard motion constraints imposed on head-neck movements. By contrast, the cable-driven robot presented in this paper does not constrain motion and enables wide-range, 6-DoF control of the head-neck. We present the mechatronic design, validation, and control implementations of this robot, as well as a human experiment to demonstrate a potential use case of this versatile robot for rehabilitation. Participants were engaged in a target reaching task while the robot applied both assistive and resistive moments on the head during the task. Our results show that neck muscle activation increased by 19% when moving the head against resistance and decreased by 28-43% when assisted by the robot. Overall, these results provide a scientific justification for further research in enabling movement and identifying personalized rehabilitation for motor training. Beyond rehabilitation, other applications such as applying force perturbations on the head to study sensory integration and applying traction to achieve pain relief may benefit from the innovation of this robotic platform which is capable of applying controlled 6-DoF forces/moments on the head.

Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.

BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will. METHODS: A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding. RESULTS: The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance. CONCLUSION: This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.

Lower-Limb Exoskeletons Appeal to Both Clinicians and Older Adults, Especially for Fall Prevention and Joint Pain Reduction.

Exoskeletons are a burgeoning technology with many possible applications to improve human life; focusing the effort of exoskeleton research and development on the most important features is essential for facilitating adoption and maximizing positive societal impact. To identify important focus areas for exoskeleton research and development, we conducted a survey with 154 potential users (older adults) and another survey with 152 clinicians. The surveys were conducted online and to ensure a consistent concept of an exoskeleton across respondents, an image of a hip exoskeleton was shown during exoskeleton-related prompts. The survey responses indicate that both older adults and clinicians are open to using exoskeletons, fall prevention and joint pain reduction are especially important features, and users are likely to wear an exoskeleton in the scenarios when it has the greatest opportunity to help prevent a fall. These findings can help inform future exoskeleton research and guide the development of devices that are accepted, used, and provide meaningful benefit to users.

Muscle-inspired bi-planar cable routing: a novel framework for designing cable driven lower limb rehabilitation exoskeletons (C-LREX).

There is a growing interest in the research and development of Cable Driven Rehabilitation Devices (CDRDs) due to multiple inherent features attractive to clinical applications, including low inertia, lightweight, high payload-to-weight ratio, large workspace, and modular design. However, previous CDRDs have mainly focused on modifying motor impairment in the sagittal plane, despite the fact that neurological disorders, such as stroke, often involve postural control and gait impairment in multiple planes. To address this gap, this work introduces a novel framework for designing a cable-driven lower limb rehabilitation exoskeleton which can assist with bi-planar impaired posture and gait. The framework used a lower limb model to analyze different cable routings inspired by human muscle architecture and attachment schemes to identify optimal routing and associated parameters. The selected cable routings were safeguarded for non-interference with the human body while generating bi-directional joint moments. The subsequent optimal cable routing model was then implemented in simulations of tracking reference healthy trajectory with bi-planar impaired gait (both in the sagittal and frontal planes). The results showed that controlling joints independently via cables yielded better performance compared to dependent control. Routing long cables through intermediate hinges reduced the peak tensions in the cables, however, at a cost of induced additional joint forces. Overall, this study provides a systematic and quantitative in silico approach, featured with accessible graphical user interface (GUI), for designing subject-specific, safe, and effective lower limb cable-driven exoskeletons for rehabilitation with options for multi-planar personalized impairment-specific features.

Clinical Delivery of Overground Exoskeleton Gait Training in Persons With Spinal Cord Injury Across the Continuum of Care: A Retrospective Analysis.

Background: After spinal cord injury (SCI), inpatient rehabilitation begins and continues through outpatient therapy. Overground exoskeleton gait training (OEGT) has been shown to be feasible in both settings, yet its use as an intervention across the continuum has not yet been reported. Objectives: This study describes OEGT for patients with SCI across the continuum and its effects on clinical outcomes. Methods: Medical records of patients with SCI who completed at least one OEGT session during inpatient and outpatient rehabilitation from 2018 to 2021 were retrospectively reviewed. Demographic data, Walking Index for Spinal Cord Injury-II (WISCI-II) scores, and OEGT session details (frequency, "walk" time, "up" time, and step count) were extracted. Results: Eighteen patients [male (83%), White (61%), aged 37.4 ± 15 years, with tetraplegia (50%), American Spinal Injury Association Impairment Scale A (28%), B (22%), C (39%), D (11%)] completed OEGT sessions (motor complete, 18.2 ± 10.3; motor incomplete, 16.7 ± 7.7) over approximately 18 weeks (motor complete, 15.1 ± 6.4; motor incomplete, 19.0 ± 8.2). Patients demonstrated improved OEGT session tolerance on device metrics including "walk" time (motor complete, 7:51 ± 4:42 to 24:50 ± 9:35 minutes; motor incomplete, 12:16 ± 6:01 to 20:01 ± 08:05 minutes), "up" time (motor complete, 16:03 ± 7:41 to 29:49 ± 12:44 minutes; motor incomplete, 16:38 ± 4:51 to 23:06 ± 08:50 minutes), and step count (motor complete, 340 ± 295.9 to 840.2 ± 379.4; motor incomplete, 372.3 ± 225.2 to 713.2 ± 272). Across therapy settings, patients with motor complete SCI experienced improvement in WISCI-II scores from 0 ± 0 at inpatient admission to 3 ± 4.6 by outpatient discharge, whereas the motor incomplete group demonstrated a change of 0.2 ± 0.4 to 9.0 ± 6.4. Conclusion: Patients completed OEGT across the therapy continuum. Patients with motor incomplete SCI experienced clinically meaningful improvements in walking function.

Estimating human joint moments unifies exoskeleton control, reducing user effort.

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.

Modularity Implications of an Overground Exoskeleton on Plantar Pressures, Strength, and Spasticity in Persons with Acquired Brain Injury.

This study explored the effects of a modular overground exoskeleton on plantar pressure distribution in healthy individuals and individuals with Acquired Brain Injury (ABI). The research involved 21 participants, including ABI patients and healthy controls, who used a unique exoskeleton with adaptable modular configurations. The primary objective was to assess how these configurations, along with factors such as muscle strength and spasticity, influenced plantar pressure distribution. The results revealed significant differences in plantar pressures among participants, strongly influenced by the exoskeleton's modularity. Notably, significant distinctions were found between ABI patients and healthy individuals. Configurations with two modules led to increased pressure in the heel and central metatarsus regions, whereas configurations with four modules exhibited higher pressures in the metatarsus and hallux regions. Future research should focus on refining and customizing rehabilitation technologies to meet the diverse needs of ABI patients, enhancing their potential for functional recovery.

Biomechanical Effects of Using a Passive Exoskeleton for the Upper Limb in Industrial Manufacturing Activities: A Pilot Study.

This study investigates the biomechanical impact of a passive Arm-Support Exoskeleton (ASE) on workers in wool textile processing. Eight workers, equipped with surface electrodes for electromyography (EMG) recording, performed three industrial tasks, with and without the exoskeleton. All tasks were performed in an upright stance involving repetitive upper limbs actions and overhead work, each presenting different physical demands in terms of cycle duration, load handling and percentage of cycle time with shoulder flexion over 80°. The use of ASE consistently lowered muscle activity in the anterior and medial deltoid compared to the free condition (reduction in signal Root Mean Square (RMS) -21.6% and -13.6%, respectively), while no difference was found for the Erector Spinae Longissimus (ESL) muscle. All workers reported complete satisfaction with the ASE effectiveness as rated on Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST), and 62% of the subjects rated the usability score as very high (>80 System Usability Scale (SUS)). The reduction in shoulder flexor muscle activity during the performance of industrial tasks is not correlated to the level of ergonomic risk involved. This preliminary study affirms the potential adoption of ASE as support for repetitive activities in wool textile processing, emphasizing its efficacy in reducing shoulder muscle activity. Positive worker acceptance and intention to use ASE supports its broader adoption as a preventive tool in the occupational sector.

Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting.

The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human-machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs.

Exercising with a robotic exoskeleton can improve memory and gait in people with Parkinson's disease by facilitating progressive exercise intensity.

People with Parkinson's disease (PwPD) can benefit from progressive high-intensity exercise facilitated with a lower-extremity exoskeleton, but the mechanisms explaining these benefits are unknown. We explored the relationship between exercise intensity progression and memory and gait outcomes in PwPD who performed 8 weeks (2 × per week) of progressive exercise with and without a lower-extremity powered exoskeleton, as the planned exploratory endpoint analysis of an open-label, parallel, pilot randomized controlled trial. Adults 50-85 years old with a confirmed diagnosis of PD participated. Twenty-seven participants randomized to exercise with (Exo = 13) or without (Nxo = 14) the exoskeleton were included in this exploratory endpoint analysis. Detailed exercise logs were kept and actigraphy was used to measure activity count*min-1 (ACPM) during all exercise sessions. Only the Exo group were able to progressively increase their ACPM over the entire 8-week intervention, whereas the Nxo group plateaued after 4 weeks. Exercise intensity progression correlated with change in the memory sub-scale of the SCOPA-COG and change in gait endurance from the 6MWT, consistent with the prevailing hypotheses linking high-intensity interval exercise to improved muscle and brain function via angiogenic and neurotrophic mechanisms. Facilitating high-intensity exercise with advanced rehabilitation technology is warranted for improving memory and gait endurance in PwPD.Registration: ClinicalTrials.gov, NCT03583879 (7/10/2018).

Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement.

Most wearable robots such as exoskeletons and prostheses can operate with dexterity, while wearers do not perceive them as part of their bodies. In this perspective, we contend that integrating environmental, physiological, and physical information through multi-modal fusion, incorporating human-in-the-loop control, utilizing neuromuscular interface, employing flexible electronics, and acquiring and processing human-robot information with biomechatronic chips, should all be leveraged towards building the next generation of wearable robots. These technologies could improve the embodiment of wearable robots. With optimizations in mechanical structure and clinical training, the next generation of wearable robots should better facilitate human motor and sensory reconstruction and enhancement.

An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach.

The utilization of robotic systems in upper limb rehabilitation has shown promising results in aiding individuals with motor impairments. This research introduces an innovative approach to enhance the efficiency and adaptability of upper limb exoskeleton robot-assisted rehabilitation through the development of an optimized stimulation control system (OSCS). The proposed OSCS integrates a fuzzy logic-based pain detection approach designed to accurately assess and respond to the patient's pain threshold during rehabilitation sessions. By employing fuzzy logic algorithms, the system dynamically adjusts the stimulation levels and control parameters of the exoskeleton, ensuring personalized and optimized rehabilitation protocols. This research conducts comprehensive evaluations, including simulation studies and clinical trials, to validate the OSCS's efficacy in improving rehabilitation outcomes while prioritizing patient comfort and safety. The findings demonstrate the potential of the OSCS to revolutionize upper limb exoskeleton-assisted rehabilitation by offering a customizable and adaptive framework tailored to individual patient needs, thereby advancing the field of robotic-assisted rehabilitation.

Design and validation of a pediatric gait assistance exoskeleton system with fast non-singular terminal sliding mode controller.

Existing exoskeletons for pediatric gait assistance have limitations in anthropometric design, structure weight, cost, user safety features, and adaptability to diverse users. Additionally, creating precise models for pediatric rehabilitation is difficult because the rapid anthropometric changes in children result in unknown model parameters. Furthermore, external disruptions, like unpredictable movements and involuntary muscle contractions, add complexity to the control schemes that need to be managed. To overcome these limitations, this study aims to develop an affordable stand-aided lower-limb exoskeleton specifically for pediatric subjects (8-12 years, 25-40 kg, 128-132 cm) in passive-assist mode. The authors modified a previously developed model (LLESv1) for improved rigidity, reduced mass, simplified motor arrangement, variable waist size, and enhanced mobility. A computer-aided design of the new exoskeleton system (LLESv2) is presented. The developed prototype of the exoskeleton appended with a pediatric subject (age: 12 years old, body mass: 40 kg, body height: 132 cm) is presented with real-time hardware architecture. Thereafter, an improved fast non-singular terminal sliding mode (IFNSTSM) control scheme is proposed, incorporating a double exponential reaching law for expedited error convergence and enhanced stability. The Lyapunov stability warrants the control system's performance despite uncertainties and disturbances. In contrast to fast non-singular terminal sliding mode (FNSTSM) control and time-scaling sliding mode (TSSM) control, experimental validation demonstrates the effectiveness of IFNSTSM control by a respective average of 5.39% and 42.1% in tracking desired joint trajectories with minimal and rapid finite time converging errors. Moreover, the exoskeleton with the proposed IFNSTSM control requires significantly lesser control efforts than the exoskeleton using contrast FNSTSM control. The Bland-Altman analysis indicates that although there is a minimal mean difference in variables when employing FNSTSM and IFNSTSM controllers, the latter exhibits significant performance variations as the mean of variables changes. This research contributes to affordable and effective pediatric gait assistance, improving rehabilitation outcomes and enhancing mobility support.

Multi Degree of Freedom Hybrid FES and Robotic Control of the Upper Limb.

Individuals who have suffered a spinal cord injury often require assistance to complete daily activities, and for individuals with tetraplegia, recovery of upper-limb function is among their top priorities. Hybrid functional electrical stimulation (FES) and exoskeleton systems have emerged as a potential solution to provide upper limb movement assistance. These systems leverage the user's own muscles via FES and provide additional movement support via an assistive exoskeleton. To date, these systems have focused on single joint movements, limiting their utility for the complex movements necessary for independence. In this paper, we extend our prior work on model predictive control (MPC) of hybrid FES-exo systems and present a multi degree of freedom (DOF) hybrid controller that uses the controller's cost function to achieve desired behavior. In studies with neurologically intact individuals, the hybrid controller is compared to an exoskeleton acting alone for movement assistance scenarios incorporating multiple degrees-of-freedom of the limb to explore the potential for exoskeleton power consumption reduction and impacts on tracking accuracy. Additionally, each scenario is explored in simulation using the models required to generate the MPC formulation. The two DOF hybrid controller implementation saw reductions in power consumption and satisfactory trajectory tracking in both the physical and simulated systems. In the four DOF implementation, the experimental results showed minor improvements for some joints of the upper limb. In simulation, we observed comparable performance as in the two DOF implementation.

Feasibility of overground exoskeleton gait training during inpatient rehabilitation after severe acquired brain injury.

OBJECTIVE: To describe the safety, feasibility, and tolerability of overground exoskeleton gait training (OEGT) integrated into clinical practice for patients after severe acquired brain injury (ABI). SETTING: Inpatient rehabilitation hospital. PARTICIPANTS: Eligible patients with severe ABI met the following criteria: age > 18, medically stable, met exoskeleton frame limitations, and a score of ≤ 3 on the motor function portion of the Coma Recovery Scale - Revised (CRS-R). Presence of consciousness disorder was not exclusionary. DESIGN: Prospective observational study. MAIN MEASURES: Outcomes examined safety (adverse events), feasibility (session count and barriers to session completion), and tolerability of OEGT (session metrics and heart rate). RESULTS: Ten patients with ABI completed 10.4 ± 4.8 OEGT sessions with no adverse events. Barriers to session completion included clinical focus on prioritized interventions. Sessions [median up time = 17 minutes, (IQR: 7); walk time = 13 minutes, (IQR: 9); step count = 243, (IQR: 161); device assist = 74, (IQR: 28.0)] were primarily spent in Very Light to Light heart rate intensities [89%, (IQR: 42%) and 9%, (IQR: 33%), respectively]. CONCLUSION: OEGT incorporated into the rehabilitation plan of care during inpatient rehabilitation after severe ABI was observed to be safe, feasible, and tolerable. However, intentional steps must be taken to facilitate patient safety.

Simulation of a control method for active kinesiotherapy with an upper extremity rehabilitation exoskeleton without force sensor.

Exoskeleton-aided active rehabilitation is a process that requires sensing and acting upon the motion intentions of the user. Typically, force sensors are used for this. However, they increase the weight and cost of these wearable devices. This paper presents the methodology for detecting users' intentions only with encoders integrated with the drives. It is unique compared to other algorithms, as enables active kinesiotherapy while adding no sensory systems. The method is based on comparing the measured motion with the one computed with the idealised model of the multibody system. The investigation assesses the method's performance and its robustness to model and measurement inaccuracies, as well as patients' unintended motions. Moreover, the PID parameters are selected to provide the optimal regulation based on the dynamics requirements. The research proves the presented concept of the control approach. For all the tests with the final settings, the system reacts to a change in the user's intention below one second and minimises the changes in proportion between the system's acceleration and the generated user's joint torque. The results are comparable to those obtained by EMG-based systems and significantly better than low-cost force sensors.