A Muscle-First, Electromechanical Hybrid Gait Restoration System in People With Spinal Cord Injury

Gait robot-assisted rehabilitation in persons with spinal cord injury: A scoping review

BACKGROUND

Many robots are available for gait rehabilitation (BWSTRT and ORET) and their application in persons with SCI allowed an improvement of walking function.

OBJECTIVE

The aim of the study is to compare the effects of different robotic exoskeletons gait training in persons with different SCI level and severity.

METHODS

Sixty-two studies were included in this systematic review; the study quality was assessed according to GRADE and PEDro's scale.

RESULTS

Quality assessment of included studies (n = 62) demonstrated a prevalence of evidence level 2; the quality of the studies was higher for BWSTRT (excellent and good) than for ORET (fair and good). Almost all persons recruited for BWSTRT had an incomplete SCI; both complete and incomplete SCI were recruited for ORET. The SCI lesion level in the persons recruited for BWSTRT are from cervical to sacral; mainly from thoracic to sacral for ORET; a high representation of AIS D lesion resulted both for BWSTRT (30%) and for ORET (45%). The walking performance, tested with 10MWT, 6MWT, TUG and WISCI, improved after exoskeleton training in persons with incomplete SCI lesions, when at least 20 sessions were applied. Persons with complete SCI lesions improved the dexterity in walking with exoskeleton, but did not recover independent walking function; symptoms such as spasticity, pain and cardiovascular endurance improved.

CONCLUSION

Different exoskeletons are available for walking rehabilitation in persons with SCI. The choice about the kind of robotic gait training should be addressed on the basis of the lesion severity and the possible comorbidities.

Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study

(1) Background: An iterative learning control (ILC) strategy was developed for a "Muscle First" Motor-Assisted Hybrid Neuroprosthesis (MAHNP). The MAHNP combines a backdrivable exoskeletal brace with neural stimulation technology to enable persons with paraplegia due to spinal cord injury (SCI) to execute ambulatory motions and walk upright. (2) Methods: The ILC strategy was developed to swing the legs in a biologically inspired ballistic fashion. It maximizes muscular recruitment and activates the motorized exoskeletal bracing to assist the motion as needed. The control algorithm was tested using an anatomically realistic three-dimensional musculoskeletal model of the lower leg and pelvis suitably modified to account for exoskeletal inertia. The model was developed and tested with the OpenSim biomechanical modeling suite. (3) Results: Preliminary data demonstrate the efficacy of the controller in swing-leg simulations and its ability to learn to balance muscular and motor contributions to improve performance and accomplish consistent stepping. In particular, the controller took 15 iterations to achieve the desired outcome with 0.3% error.

Closed-Loop Torque and Kinematic Control of a Hybrid Lower-Limb Exoskeleton for Treadmill Walking

Restoring and improving the ability to walk is a top priority for individuals with movement impairments due to neurological injuries. Powered exoskeletons coupled with functional electrical stimulation (FES), called hybrid exoskeletons, exploit the benefits of activating muscles and robotic assistance for locomotion. In this paper, a cable-driven lower-limb exoskeleton is integrated with FES for treadmill walking at a constant speed. A nonlinear robust controller is used to activate the quadriceps and hamstrings muscle groups via FES to achieve kinematic tracking about the knee joint. Moreover, electric motors adjust the knee joint stiffness throughout the gait cycle using an integral torque feedback controller. For the hip joint, a robust sliding-mode controller is developed to achieve kinematic tracking using electric motors. The human-exoskeleton dynamic model is derived using Lagrangian dynamics and incorporates phase-dependent switching to capture the effects of transitioning from the stance to the swing phase, and vice versa. Moreover, low-level control input switching is used to activate individual muscles and motors to achieve flexion and extension about the hip and knee joints. A Lyapunov-based stability analysis is developed to ensure exponential tracking of the kinematic and torque closed-loop error systems, while guaranteeing that the control input signals remain bounded. The developed controllers were tested in real-time walking experiments on a treadmill in three able-bodied individuals at two gait speeds. The experimental results demonstrate the feasibility of coupling a cable-driven exoskeleton with FES for treadmill walking using a switching-based control strategy and exploiting both kinematic and force feedback.

Adaptation Strategies for Personalized Gait Neuroprosthetics

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.

Transmission Comparison for Cooperative Robotic Applications

The development of powered assistive devices that integrate exoskeletal motors and muscle activation for gait restoration benefits from actuators with low backdrive torque. Such an approach enables motors to assist as needed while maximizing the joint torque muscles, contributing to movement, and facilitating ballistic motions instead of overcoming passive dynamics. Two electromechanical actuators were developed to determine the effect of two candidate transmission implementations for an exoskeletal joint. To differentiate the transmission effects, the devices utilized the same motor and similar gearing. One actuator included a commercially available harmonic drive transmission while the other incorporated a custom designed two-stage planetary transmission. Passive resistance and mechanical efficiency were determined based on isometric torque and passive resistance. The planetary-based actuator outperformed the harmonic-based actuator in all tests and would be more suitable for hybrid exoskeletons.

Neural Networks Trained via Reinforcement Learning Stabilize Walking of a Three-Dimensional Biped Model With Exoskeleton Applications

Our group is developing a cyber-physical walking system (CPWS) for people paralyzed by spinal cord injuries (SCI). The current CPWS consists of a functional neuromuscular stimulation (FNS) system and a powered lower-limb exoskeleton for walking with leg movements in the sagittal plane. We are developing neural control systems that learn to assist the user of this CPWS to walk with stability. In a previous publication (Liu et al., Biomimetics, 2019, 4, 28), we showed a neural controller that stabilized a simulated biped in the sagittal plane. We are considering adding degrees of freedom to the CPWS to allow more natural walking movements and improved stability. Thus, in this paper, we present a new neural network enhanced control system that stabilizes a three-dimensional simulated biped model of a human wearing an exoskeleton. Results show that it stabilizes human/exoskeleton models and is robust to impact disturbances. The simulated biped walks at a steady pace in a range of typical human ambulatory speeds from 0.7 to 1.3 m/s, follows waypoints at a precision of 0.3 m, remains stable, and continues walking forward despite impact disturbances and adapts its speed to compensate for persistent external disturbances. Furthermore, the neural network controller stabilizes human models of different statures from 1.4 to 2.2 m tall without any changes to the control parameters. Please see videos at the following link: 3D biped walking control.