Physiotherapy using a free-standing robotic exoskeleton for patients with spinal cord injury: a feasibility study

Exoskeleton-Assisted Rehabilitation and Neuroplasticity in Spinal Cord Injury

Spinal cord injury (SCI) results in neurological deficits below the level of injury, causing motor dysfunction and various severe multisystem complications. Rehabilitative training plays a crucial role in the recovery of individuals with SCI, and exoskeleton serves as an emerging and promising tool for rehabilitation, especially in promoting neuroplasticity and alleviating SCI-related complications. This article reviews the classifications and research progresses of medical exoskeletons designed for SCI patients and describes their performances in practical application separately. Meanwhile, we discuss their mechanisms for enhancing neuroplasticity and functional remodeling, as well as their palliative impacts on secondary complications. The potential trends in exoskeleton design are raised according to current progress and requirements on SCI rehabilitation.

Balance recovery for lower limb exoskeleton in standing posture based on orbit energy analysis

Introduction: The need for effective balance control in lower limb rehabilitation exoskeletons is critical for ensuring stability and safety during rehabilitation training. Current research into specialized balance recovery strategies is limited, highlighting a gap in biomechanics-inspired control methods. Methods: We introduce a new metric called "Orbit Energy" (OE), which assesses the balance state of the human-exoskeleton system based on the dynamics of the overall center of mass. Our control framework utilizes OE to choose appropriate balance recovery strategies, including torque controls at the ankle and hip joints. Results: The efficacy of our control algorithm was confirmed through Matlab Simulink simulations, which analyzed the recovery of balance under various disturbance forces and conditions. Further validation came from physical experiments with human subjects wearing the exoskeleton, where a significant reduction in muscle activation was observed during balance maintenance under external disturbances. Discussion: Our findings underscore the potential of biomechanics-inspired metrics like OE in enhancing exoskeleton functionality for rehabilitation purposes. The introduction of such metrics could lead to more targeted and effective balance recovery strategies, ultimately improving the safety and stability of exoskeleton use in rehabilitation settings.

IMU-Based Real-Time Estimation of Gait Phase Using Multi-Resolution Neural Networks

This work presents a real-time gait phase estimator using thigh- and shank-mounted inertial measurement units (IMUs). A multi-rate convolutional neural network (CNN) was trained to estimate gait phase for a dataset of 16 participants walking on an instrumented treadmill with speeds varying between 0.1 to 1.9 m/s, and conditions such as asymmetric walking, stop-start, and sudden speed changes. One-subject-out cross-validation was used to assess the robustness of the estimator to the gait patterns of new individuals. The proposed model had a spatial root mean square error of 5.00±1.65%, and a temporal mean absolute error of 2.78±0.97% evaluated at the heel strike. A second cross-validation was performed to show that leaving out any of the walking conditions from the training dataset did not result in significant performance degradation. A 2-sample Kolmogorov-Smirnov test showed that there was no significant increase in spatial or temporal error when testing on the abnormal walking conditions left out of the training set. The results of the two cross-validations demonstrate that the proposed model generalizes well across new participants, various walking speeds, and gait patterns, showcasing its potential for use in investigating patient populations with pathological gaits and facilitating robot-assisted walking.

Combining robot-assisted therapy with virtual reality or using it alone? A systematic review on health-related quality of life in neurological patients

BACKGROUND

In the field of neurorehabilitation, robot-assisted therapy (RAT) and virtual reality (VR) have so far shown promising evidence on multiple motor and functional outcomes. The related effectiveness on patients' health-related quality of life (HRQoL) has been investigated across neurological populations but still remains unclear. The present study aimed to systematically review the studies investigating the effects of RAT alone and with VR on HRQoL in patients with different neurological diseases.

METHODS

A systematic review of the studies evaluating the impact of RAT alone and combined with VR on HRQoL in patients affected by neurological diseases (i.e., stroke, multiple sclerosis, spinal cord injury, Parkinson's Disease) was conducted according to PRISMA guidelines. Electronic searches of PubMed, Web of Science, Cochrane Library, CINAHL, Embase, and PsychINFO (2000-2022) were performed. Risk of bias was evaluated through the National Institute of Health Quality Assessment Tool. Descriptive data regarding the study design, participants, intervention, rehabilitation outcomes, robotic device typology, HRQoL measures, non-motor factors concurrently investigated, and main results were extracted and meta-synthetized.

RESULTS

The searches identified 3025 studies, of which 70 met the inclusion criteria. An overall heterogeneous configuration was found regarding the study design adopted, intervention procedures and technological devices implemented, rehabilitation outcomes (i.e., related to both upper and lower limb impairment), HRQoL measures administered, and main evidence. Most of the studies reported significant effects of both RAT and RAT plus VR on patients HRQoL, whether they adopted generic or disease-specific HRQoL measures. Significant post-intervention within-group changes were mainly found across neurological populations, while fewer studies reported significant between-group comparisons, and then, mostly in patients with stroke. Longitudinal investigations were also observed (up to 36 months), but significant longitudinal effects were exclusively found in patients with stroke or multiple sclerosis. Finally, concurrent evaluations on non-motor outcomes beside HRQoL included cognitive (i.e., memory, attention, executive functions) and psychological (i.e., mood, satisfaction with the treatment, device usability, fear of falling, motivation, self-efficacy, coping, and well-being) variables.

CONCLUSIONS

Despite the heterogeneity observed among the studies included, promising evidence was found on the effectiveness of RAT and RAT plus VR on HRQoL. However, further targeted short- and long-term investigations, are strongly recommended for specific HRQoL subcomponents and neurological populations, through the adoption of defined intervention procedures and disease-specific assessment methodology.

[New technologies and robotics]

The development of increasingly more complex computer and electromotor technologies enables the increasing use and expansion of robot-assisted systems in trauma surgery rehabilitation; however, the currently available devices are rarely comprehensively applied but are often used within pilot projects and studies. Different technological approaches, such as exoskeletal systems, functional electrical stimulation, soft robotics, neurorobotics and brain-machine interfaces are used and combined to read and process the communication between, e.g., residual musculature or brain waves, to transfer them to the executing device and to enable the desired execution.Currently, the greatest amount of evidence exists for the use of exoskeletal systems with different modes of action in the context of gait and stance rehabilitation in paraplegic patients; however, their use also plays a role in the rehabilitation of fractures close to the hip joint and endoprosthetic care. So-called single joint systems are also being tested in the rehabilitation of functionally impaired extremities, e.g., after knee prosthesis implantation. At this point, however, the current data situation is still too limited to be able to make a clear statement about the use of these technologies in the trauma surgery "core business" of rehabilitation after fractures and other joint injuries.For rehabilitation after limb amputation, in addition to the further development of myoelectric prostheses, the current development of "sentient" prostheses is of great interest. The use of 3D printing also plays a role in the production of individualized devices.Due to the current progress of artificial intelligence in all fields, ground-breaking further developments and widespread application possibilities in the rehabilitation of trauma patients are to be expected.

Rehabilitation Program for Gait Training Using UAN.GO, a Powered Exoskeleton: A Case Report

Background: Spinal cord injury is characterized by the interruption of neural pathways of the spinal cord, with alteration of sensory, motor, and autonomic functions. Robotic-assisted gait training offers many possibilities, including the capability to reach a physiological gait pattern. Methods: A training protocol with UAN.GO^®, an active lower limb exoskeleton, was developed. A participant having D10 complete SCI was recruited for this study. The training protocol was composed by 13 sessions, lasting 1.5 h each. The effectiveness of the protocol was evaluated through the mobility performance during the 6 MWT, the level of exertion perceived administrating Borg RPE at the end of each 6 MWT. Furthermore, time and effort required by the participant to earn a higher level of skills were considered. Results: A significant improvement was registered in the six MWT (t₀ = 45.64 m t₁ = 84.87 m). Data referring to the mean level of exertion remained stable. The patient successfully achieved a higher level of independence and functional mobility with the exoskeleton. Discussion: The findings from this preliminary study suggest that UAN.GO can be a valid tool for walking rehabilitation of spinal cord injury patients, allowing the achievement of greater mobility performances.