Upper limb robotics applied to neurorehabilitation: An overview of clinical practice

Transcutaneous Cervical Spinal Cord Stimulation Combined with Robotic Exoskeleton Rehabilitation for the Upper Limbs in Subjects with Cervical SCI: Clinical Trial

(1) Background: Restoring arm and hand function is a priority for individuals with cervical spinal cord injury (cSCI) for independence and quality of life. Transcutaneous spinal cord stimulation (tSCS) promotes the upper extremity (UE) motor function when applied at the cervical region. The aim of the study was to determine the effects of cervical tSCS, combined with an exoskeleton, on motor strength and functionality of UE in subjects with cSCI. (2) Methods: twenty-two subjects participated in the randomized mix of parallel-group and crossover clinical trial, consisting of an intervention group (n = 15; tSCS exoskeleton) and a control group (n = 14; exoskeleton). The assessment was carried out at baseline, after the last session, and two weeks after the last session. We assessed graded redefined assessment of strength, sensibility, and prehension (GRASSP), box and block test (BBT), spinal cord independence measure III (SCIM-III), maximal voluntary contraction (MVC), ASIA impairment scale (AIS), and WhoQol-Bref; (3) Results: GRASSP, BBT, SCIM III, cylindrical grip force and AIS motor score showed significant improvement in both groups (p ≤ 0.05), however, it was significantly higher in the intervention group than the control group for GRASSP strength, and GRASSP prehension ability (p ≤ 0.05); (4) Conclusion: our findings show potential advantages of the combination of cervical tSCS with an exoskeleton to optimize the outcome for UE.

Rehabilitation robotics after stroke: a bibliometric literature review

INTRODUCTION

Stroke is the leading cause of long-term disability in developed countries. Due to population aging, the number of people requiring rehabilitation after stroke is going to rise in the coming decades. Robot-mediated neurorehabilitation has the potential to improve clinical outcomes of rehabilitation treatments. A statistical analysis of the literature aims to focus on the main trend of this topic.

AREAS COVERED

A bibliometric survey on post-stroke robotic rehabilitation was performed through a database collection of scientific publications in the field of rehabilitation robotics. By covering the last 20 years, 17429 sources were collected. Relevant patterns and statistics concerning the main research areas were analyzed. Leading journals and conferences which publish and disseminate knowledge in the field were identified. A detailed nomenclature study was carried out. The time trends of the research field were captured. Opinions and predictions of future trends that are expected to shape the near future of the field were discussed.

EXPERT OPINION

Data analysis reveals the continuous expansion of the research field over the last two decades, which is expected to rise considerably in near future. More attention will be paid to the lower limbs rehabilitation and disease/design specific applications in early-stage patients.

Muscle weakness assessment tool for automated therapy selection in elbow rehabilitation

Clinical observations and subjective judgements have traditionally been used to evaluate patients with muscular and neurological disorders. As a result, identifying and analyzing functional improvements are difficult, especially in the absence of expertise. Quantitative assessment, which serves as the motivation for this study, is an essential prerequisite to forecast the task of the rehabilitation device in order to develop rehabilitation training. This work provides a quantitative assessment tool for muscle weakness in the human upper limbs for robotic-assisted rehabilitation. The goal is to map the assessment metrics to the recommended rehabilitation exercises. Measurable interaction forces and muscle correlation factors are the selected parameters to design a framework for muscular nerve cell condition detection and appropriate limb trajectory selection. In this work, a data collection setup is intended for extracting muscle intervention and assessment using MyoMeter, Goniometer and surface electromyography data for upper limbs. Force signals and human physiological response data are evaluated and categorized to infer the relevant progress. Based upon the most influencing muscles, curve fitting is performed. Trajectory-based data points are collected through a scaled geometric Open-Sim musculoskeletal model that fits the subject’s anthropometric data. These data are found to be most suitable to prescribe relevant exercise and to design customized robotic assistance. Case studies demonstrate the approach’s efficacy, including optimally synthesized automated configuration for the desired trajectory.

Robot-Assisted Therapy Combined with Trunk Restraint in Acute Stroke Patients: A Randomized Controlled Study

BACKGROUND

Reducing the compensatory mechanism by restraining unnecessary trunk movements may help enhance the effectiveness of robot-assisted therapy.

OBJECTIVE

This study aimed to investigate the effects of robot-assisted therapy in combination with trunk restraint on upper extremity function and on daily activities in patients with acute stroke (≤ 30days of onset).

METHODS

Thirty-six acute stroke patients were randomly assigned to an experimental (n=18) or control (n=18) group. The experimental group performed robot-assisted therapy combined with trunk restraint, while the control group performed only robot-assisted therapy. Both groups were treated for 30 min, 5 days a week, for a total of 3 weeks. The outcome measures included the Fugl-Meyer assessment upper extremity, wolf motor function test, motor activity log, upper extremity muscle strength, and modified Barthel index.

RESULTS

After the intervention, both groups showed significant improvements in Fugl-Meyer assessment upper extremity, wolf motor function test, motor activity log, elbow extensor muscle strength, and modified Barthel index (p < 0.05). Post-intervention, the experimental group exhibited greater changes in the Fugl-Meyer assessment upper extremity, motor activity log, and elbow extensor muscle strength (p < 0.05).

CONCLUSION

Our study suggests that robot-assisted therapy in combination with trunk restraint is more effective for improving upper extremity function than only robot-assisted therapy in acute stroke patients.

Discussion on the Rehabilitation of Stroke Hemiplegia Based on Interdisciplinary Combination of Medicine and Engineering

Interdisciplinary combinations of medicine and engineering are part of the strategic plan of many universities aiming to be world-class institutions. One area in which these interactions have been prominent is rehabilitation of stroke hemiplegia. This article reviews advances in the last five years of stroke hemiplegia rehabilitation via interdisciplinary combination of medicine and engineering. Examples of these technologies include VR, RT, mHealth, BCI, tDCS, rTMS, and TCM rehabilitation. In this article, we will summarize the latest research in these areas and discuss the advantages and disadvantages of each to examine the frontiers of interdisciplinary medicine and engineering advances.

Multimodal treatment for spinal cord injury: a sword of neuroregeneration upon neuromodulation

Spinal cord injury is linked to the interruption of neural pathways, which results in irreversible neural dysfunction. Neural repair and neuroregeneration are critical goals and issues for rehabilitation in spinal cord injury, which require neural stem cell repair and multimodal neuromodulation techniques involving personalized rehabilitation strategies. Besides the involvement of endogenous stem cells in neurogenesis and neural repair, exogenous neural stem cell transplantation is an emerging effective method for repairing and replacing damaged tissues in central nervous system diseases. However, to ensure that endogenous or exogenous neural stem cells truly participate in neural repair following spinal cord injury, appropriate interventional measures (e.g., neuromodulation) should be adopted. Neuromodulation techniques, such as noninvasive magnetic stimulation and electrical stimulation, have been safely applied in many neuropsychiatric diseases. There is increasing evidence to suggest that neuromagnetic/electrical modulation promotes neuroregeneration and neural repair by affecting signaling in the nervous system; namely, by exciting, inhibiting, or regulating neuronal and neural network activities to improve motor function and motor learning following spinal cord injury. Several studies have indicated that fine motor skill rehabilitation training makes use of residual nerve fibers for collateral growth, encourages the formation of new synaptic connections to promote neural plasticity, and improves motor function recovery in patients with spinal cord injury. With the development of biomaterial technology and biomechanical engineering, several emerging treatments have been developed, such as robots, brain-computer interfaces, and nanomaterials. These treatments have the potential to help millions of patients suffering from motor dysfunction caused by spinal cord injury. However, large-scale clinical trials need to be conducted to validate their efficacy. This review evaluated the efficacy of neural stem cells and magnetic or electrical stimulation combined with rehabilitation training and intelligent therapies for spinal cord injury according to existing evidence, to build up a multimodal treatment strategy of spinal cord injury to enhance nerve repair and regeneration.

Haptic vs sensorimotor training in the treatment of upper limb dysfunction in multiple sclerosis: A multi-center, randomised controlled trial

BACKGROUND

In multiple sclerosis (MS) exercise improves upper limb functions, but it is unclear what training types are more effective.

OBJECTIVE

This study compares robot-assisted training based on haptic or sensorimotor exercise.

METHODS

41clinically definite MS subjects with upper limb impairment were randomised into two groups: (i) Haptic and (ii) Sensorimotor. Subjects in the Haptic performed a robot-assisted training protocol designed to counteract incoordination and weakness. The task -interaction with a virtual mass-spring system against a resistive load- requires coordination skills. Task difficulty and magnitude of resistive load were automatically adjusted to the individual impairment. Subjects in the Sensorimotor performed reaching movements under visual control; the robot generated no forces. Both groups underwent eight training sessions (40 min/session, 2 sessions/week). Treatment outcome were 9HPT and ARAT scores.

RESULTS

The average 9HPT score decreased from 74±9 s to 61±8 s for the Haptic and from 49±6 s to 44±6 s. We found a significant Treatment (p=.0453) and Time differences (p=.005), but no significant Treatment×Time interactions although we found that the absolute change was only significant in the Haptic group (p=.011). We observed no significant changes in the ARAT score. Participants tolerated treatments well with a low drop-out rate. In the subjects evaluated at after 12 week (11 subject in sensory-motor and 17 in haptic group) no retention of the effect was found.

CONCLUSIONS

Task oriented training may improve upper limb function in persons with MS especially in prevalent pyramidal impaired subjects without maintain the effects after three months.

CLINICAL TRIAL REGISTRATION NUMBER

NCT02711566 (clinicaltrial.gov).

Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy

OBJECTIVES

Compensations are commonly employed by patients with stroke during rehabilitation without therapist supervision, leading to suboptimal recovery outcomes. This study investigated the feasibility of the real-time monitoring of compensation in patients with stroke by using pressure distribution data and machine learning algorithms. Whether trunk compensation can be reduced by combining the online detection of compensation and haptic feedback of a rehabilitation robot was also investigated.

METHODS

Six patients with stroke did three forms of reaching movements while pressure distribution data were recorded as Dataset1. A support vector machine (SVM) classifier was trained with features extracted from Dataset1. Then, two other patients with stroke performed reaching tasks, and the SVM classifier trained by Dataset1 was employed to classify the compensatory patterns online. Based on the real-time monitoring of compensation, a rehabilitation robot provided an assistive force to patients with stroke to reduce compensations.

RESULTS

Good classification performance (F1 score > 0.95) was obtained in both offline and online compensation analysis using the SVM classifier and pressure distribution data of patients with stroke. Based on the real-time detection of compensatory patterns, the angles of trunk rotation, trunk lean-forward and trunk-scapula elevation decreased by 46.95%, 32.35% and 23.75%, respectively.

CONCLUSION

High classification accuracies verified the feasibility of detecting compensation in patients with stroke based on pressure distribution data. Since the validity and reliability of the online detection of compensation has been verified, this classifier can be incorporated into a rehabilitation robot to reduce trunk compensations in patients with stroke.

Assist-as-Needed Controller to a Task-based Knee Rehabilitation Exoskeleton

This research aims to design and implement an assist-as-needed controller and patient recovery tracking system into a novel task based knee rehabilitation exoskeleton device. The level of support from the exoskeleton is measured through the force sensing resistors (FSR) placed in the interface of lower- leg and the exoskeleton. The signal from the FSR is used as a feedback to control the actuator torque. The intent of the user to start, stop, move left, and right are associated with muscle activities, surface electromyography (sEMG) signals in the upper-leg. The preliminary results have shown that the system has provided the user a visual display of the amount of recovery and history while providing full autonomy to control the exoskeleton device. The successful implementation of these features can eliminate the need of constant supervision, and hence saves time and reduces cost of the rehabilitation process; which can also be used in a home-based or telerehabilitation settings.