Volition-adaptive control for gait training using wearable exoskeleton: preliminary tests with incomplete spinal cord injury individuals

Effectiveness of Intelligent Control Strategies in Robot-Assisted Rehabilitation-A Systematic Review

This review aims to provide a systematic analysis of the literature focused on the use of intelligent control systems in robotics for physical rehabilitation, identifying trends in recent research and comparing the effectiveness of intelligence used in control, with the aim of determining important factors in robot-assisted rehabilitation and how intelligent controller design can improve them. Seven electronic research databases were searched for articles published in the years 2015 - 2022 with articles selected based on relevance to the subject area of intelligent control systems in rehabilitation robotics. It was found that the most common use of intelligent algorithms for control is improving traditional control strategies with optimization and learning techniques. Intelligent algorithms are also commonly used in sensor output mapping, model construction, and for various data learning purposes. Experimental results show that intelligent controllers consistently outperform non-intelligent controllers in terms of transparency, tracking accuracy, and adaptability. Active participation of the patients and lowered interaction forces are consistently mentioned as important factors in improving the rehabilitation outcome as well as the patient experience. However, there are limited examples of studies presenting experimental results with impaired participants suffering limited range of motion, so the effectiveness of therapy provided by these systems is often difficult to quantify. A lack of universal evaluation criteria also makes it difficult to compare control systems outside of articles which use their own comparison criteria.

Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton

This study explores the use of a brain-computer interface (BCI) based on motor imagery (MI) for the control of a lower limb exoskeleton to aid in motor recovery after a neural injury. The BCI was evaluated in ten able-bodied subjects and two patients with spinal cord injuries. Five able-bodied subjects underwent a virtual reality (VR) training session to accelerate training with the BCI. Results from this group were compared with a control group of five able-bodied subjects, and it was found that the employment of shorter training by VR did not reduce the effectiveness of the BCI and even improved it in some cases. Patients gave positive feedback about the system and were able to handle experimental sessions without reaching high levels of physical and mental exertion. These results are promising for the inclusion of BCI in rehabilitation programs, and future research should investigate the potential of the MI-based BCI system.

Corticomuscular Connectivity during Walking in Able Bodied and Individuals with Incomplete Spinal Cord Injury

This exploratory study used EEG as mobile imaging method to study cortico-muscular connectivity (CMC) during walking in able-bodied individuals (AB) and individuals with spinal cord injury (iSCI), while walking with and without exoskeleton walking robot (EWR) assistance. We also explored change in CMC after intensive training using EWR assistance in iSCI. Results showed no different in CMC within the AB group during walking with and without robot assistance. However, before training the iSCI subjects showed lower CMC during walking with robot assistance. The intensive 40 hours of walking training with EWR improved the walking function in iSCI participants allowing them to walk with robot assistance set to lower assistance level. This decrease in assistance level and improvement in walking function correlated with increase in CMC, reducing the difference in CMC during walking with and without EWR assistance. The findings suggest that high level of robot assistance and low walking function in iSCI correlates with weaker connectivity between primary motor cortices and lower extremity muscles. Further research is needed to better understand the importance of intention and cortical involvement in training of walking function using EWRs. Clinical Relevance - This study provides innovative data on CMC during walking and how it changes with EWR assistance and with training. This research is important to the clinical field to provide recommendations of how training of walking function can be delivered to maximize cortical engagement and improve rehabilitation outcomes.

Effects of Individualized Gait Rehabilitation Robotics for Gait Training on Hemiplegic Patients: Before-After Study in the Same Person

Background

Lower-limb exoskeleton robots are being widely used in gait rehabilitation training for patients with stroke. However, most of the current rehabilitation robots are guided by predestined gait trajectories, which are often different from the actual gait trajectories of specific patients. One solution is to train patients using individualized gait trajectories generated from the physical parameters of patients. Hence, we aimed to explore the effect of individual gaits on energy consumption situations during gait rehabilitation training for hemiplegic patients with lower-limb exoskeleton robots.

Methods

A total of 9 unilateral-hemiplegic patients were recruited for a 2-day experiment. On the first day of the experiment, the 9 patients were guided by a lower-limb exoskeleton robot, walking on flat ground for 15 min in general gait trajectory, which was gained by clinical gait analysis (CGA) method. On the other day, the same 9 patients wore the identical robot and walked on the same flat ground for 15 min in an individualized gait trajectory. The main physiological parameters including heart rate (HR) and peripheral capillary oxygen saturation (SpO2) were acquired via cardio tachometer and oximeter before and after the walking training. The energy consumption situation was indicated by the variation of the value of HR and SpO2 after walking training compared to before.

Results

Between-group comparison showed that the individualized gait trajectory training resulted in an increase in HR levels and a decrease in SpO2 levels compared to the general gait trajectory training. The resulting difference had a statistical significance of p < 0.05.

Conclusion

Using individualized gait guidance in rehabilitation walking training can significantly improve energy efficiency for hemiplegic patients with stroke.

A BMI Based on Motor Imagery and Attention for Commanding a Lower-Limb Robotic Exoskeleton: A Case Study

Lower-limb robotic exoskeletons are wearable devices that can be beneficial for people with lower-extremity motor impairment because they can be valuable in rehabilitation or assistance. These devices can be controlled mentally by means of brain–machine interfaces (BMI). The aim of the present study was the design of a BMI based on motor imagery (MI) to control the gait of a lower-limb exoskeleton. The evaluation is carried out with able-bodied subjects as a preliminary study since potential users are people with motor limitations. The proposed control works as a state machine, i.e., the decoding algorithm is different to start (standing still) and to stop (walking). The BMI combines two different paradigms for reducing the false triggering rate (when the BMI identifies irrelevant brain tasks as MI), one based on motor imagery and another one based on the attention to the gait of the user. Research was divided into two parts. First, during the training phase, results showed an average accuracy of 68.44 ± 8.46% for the MI paradigm and 65.45 ± 5.53% for the attention paradigm. Then, during the test phase, the exoskeleton was controlled by the BMI and the average performance was 64.50 ± 10.66%, with very few false positives. Participants completed various sessions and there was a significant improvement over time. These results indicate that, after several sessions, the developed system may be employed for controlling a lower-limb exoskeleton, which could benefit people with motor impairment as an assistance device and/or as a therapeutic approach with very limited false activations.

Pseudo-Online BMI Based on EEG to Detect the Appearance of Sudden Obstacles during Walking

The aim of this paper is to describe new methods for detecting the appearance of unexpected obstacles during normal gait from EEG signals, improving the accuracy and reducing the false positive rate obtained in previous studies. This way, an exoskeleton for rehabilitation or assistance of people with motor limitations commanded by a Brain-Machine Interface (BMI) could be stopped in case that an obstacle suddenly appears during walking. The EEG data of nine healthy subjects were collected during their normal gait while an obstacle appearance was simulated by the projection of a laser line in a random pattern. Different approaches were considered for selecting the parameters of the BMI: subsets of electrodes, time windows and classifier probabilities, which were based on a linear discriminant analysis (LDA). The pseudo-online results of the BMI for detecting the appearance of obstacles, with an average percentage of 63.9% of accuracy and 2.6 false positives per minute, showed a significant improvement over previous studies.

Design and effectiveness evaluation of mirror myoelectric interfaces: a novel method to restore movement in hemiplegic patients

The motor impairment occurring after a stroke is characterized by pathological muscle activation patterns or synergies. However, while robot-aided myoelectric interfaces have been proposed for stroke rehabilitation, they do not address this issue, which might result in inefficient interventions. Here, we present a novel paradigm that relies on the correction of the pathological muscle activity as a way to elicit rehabilitation, even in patients with complete paralysis. Previous studies demonstrated that there are no substantial inter-limb differences in the muscle synergy organization of healthy individuals. We propose building a subject-specific model of muscle activity from the healthy limb and mirroring it to use it as a learning tool for the patient to reproduce the same healthy myoelectric patterns on the paretic limb during functional task training. Here, we aim at understanding how this myoelectric model, which translates muscle activity into continuous movements of a 7-degree of freedom upper limb exoskeleton, could transfer between sessions, arms and tasks. The experiments with 8 healthy individuals and 2 chronic stroke patients proved the feasibility and effectiveness of such myoelectric interface. We anticipate the proposed method to become an efficient strategy for the correction of maladaptive muscle activity and the rehabilitation of stroke patients.

Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.