Nothing here that will help survivors recover. Useless.
Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness
Journal of NeuroEngineering and Rehabilitation volume 20, Article number: 23 (2023)
Abstract
Background
In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes.
Methods
Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy.
Results
(1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke.
Conclusions
Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients’ specific pathology outperform current control strategies.
Background
Brain injury is a wide open concept associated with damage to the brain due to events inside of the body, i.e., non-traumatic brain injuries, or external forces, i.e., traumatic brain injuries (TBIs). Non-traumatic brain injuries include stroke or cerebral palsy. Brain injuries are one of the major causes of death and disability worldwide [1]. The global incidence of stroke increases by more than 13.7 million new cases each year [2], and is the third leading cause of disability worldwide [3]. The prevalence of cerebral palsy is estimated to be from nearly 2 to nearly 3 per 1000 newborns worldwide [4, 5]. Traumatic brain injury is another leading cause of disability around the globe, with 69 million survivors every year [6].
Difficulty in standing and walking is one of the major consequences of brain injuries. For instance, over 63% of stroke survivors suffer from half-mild to severe motor and cognitive disabilities [7], and 30–36% are unable to walk without assistive aids [8, 9]. This results in loss of independent mobility and limits community participation and social integration, which causes secondary health conditions [10]. Individuals with brain injuries can exhibit common motor impairments, like paralysis, spasticity, or abnormal muscle synergies, leading to compensatory movements and gait asymmetries [11,12,13,14,15]. This pathological gait hinders a skilful, comfortable, safe, and metabolically efficient ambulation [16].
The recovery process after a brain injury takes months to years and neurological impairments can be permanent [17]. There is strong evidence that early, intensive, and repetitive task- and goal-oriented training, which is progressively adapted to the patients level of impairment and rehabilitation stage, can improve functional ambulatory outcomes [11, 18,19,20,21,22,23]. However, due to limited resources and the heterogeneity of impairment, it is challenging for physiotherapists to provide the required intensity and dose of training, while extracting quantitative information to maximize functional walking ability for a specific patient.
Robotics can play a promising role in gait rehabilitation for individuals with brain injuries. Robots allow performance of wide range of tasks—e.g., walking, sitting up/down, or walking on a slope—with high intensity. Some robotic controllers might also promote patients’ active participation and engagement during the training process, e.g., by varying the level of the assistive force [24, 25]. High repeatability and intensity of training, together with patients’ engagement, have been listed as crucial factors to induce neural plasticity and motor learning [26,27,28]. Importantly, clinical evidence suggests that combining robotic and conventional rehabilitation training positively impacts the ability to walk independently, walking speed, and walking capacity, although there is still no solid evidence about the superiority of robotic rehabilitation over conventional therapy [29,30,31,32,33].
Lower-limb exoskeletons promote task-oriented repetitive movements, muscle strengthening, and movement coordination, which have been shown to positively impact energy efficiency, gait speed, and balance control [34, 35]. Exoskeletons, compared to other robotic solutions, e.g., patient-guided suspension systems and end-effector devices, allow for full control of the leg joint angles and torques, and are the preferred robotic solutions for training brain-injured patients who suffer from severe motor disabilities [36]. Thereby, we consider that focusing on exoskeleton technology is a wide and rich enough topic to extract conclusions on the clinical effectiveness of the control strategies in the broad group of brain-injured patients [37,38,39].
The interest on lower-limb exoskeletons for gait rehabilitation has increased exponentially in the last years, which is reflected in the considerable number of reviews published within the last decade [38, 40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60]. However, the majority of these reviews focus on hardware, while only a few of them analyzed the control strategies implemented on lower limb exoskeletons and their effects on walking function in individuals with brain injuries [38, 41, 42, 54,55,56,57,58,59,60]. Yet, the control strategy—as ergonomics and robot actuation—might play a key role on the effectiveness of the robotic treatment [61]. As in every biological system, control rules are essential to modulate every action attending to internal and external factors [62].
We found a few literature surveys that focused on control strategies for lower-extremity exoskeletons: Baud et al. and Li et al. categorised the control strategies and actuation systems implemented on lower-limb exoskeletons [41, 42]; Chen et al. presented a review on wearable hip exoskeletons for gait rehabilitation and human performance augmentation that addressed actuation system technologies and control strategies [57]; Zhang et al. presented a review on lower-limb exoskeletons offering details about actuation systems, high-level control, and human–robot synchronization tools [38]; Tucker et al. [55] reviewed several control strategies, gait pattern recognition, and biofeedback approaches for lower extremity robotic prosthetics and orthotics. Finally, a recent systematic review on wearable ankle rehabilitation robots for post-stroke rehabilitation focused on actuation technologies, gait event detection, control strategies, and the clinical effects of the robotic intervention [59].
In this systematic review, we aim at complementing previous literature surveys by providing an updated structured framework of current control strategies, analyzing the methodology of clinical validations used in the robotic interventions, and reporting the potential relation between the employed control strategies and clinical outcomes. In this literature survey we seek to answer the following three research questions: (1) Which control strategies have been used on powered lower limb exoskeletons for individuals with brain injuries?, (2) What are the experimental protocols and outcome metrics used in the clinical validation of robotic interventions?, and (3) What is the current clinical evidence on the effectiveness of the different control strategies?
More at link.
No comments:
Post a Comment