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.

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.

Effect of Joint Friction Compensation on a "Muscle-First" Motor-Assisted Hybrid Neuroprosthesis.

This study assessed the metabolic energy consumption of walking with the external components of a "Muscle-First" Motor Assisted Hybrid Neuroprosthesis (MAHNP), which combines implanted neuromuscular stimulation with a motorized exoskeleton. The "Muscle-First" approach prioritizes generating motion with the wearer's own muscles via electrical stimulation with the actuators assisting on an as-needed basis. The motorized exoskeleton contributes passive resistance torques at both the hip and knee joints of 6Nm and constrains motions to the sagittal plane. For the muscle contractions elicited by neural stimulation to be most effective, the motorized joints need to move freely when not actively assisting the desired motion. This study isolated the effect of the passive resistance or "friction" added at the joints by the assistive motors and transmissions on the metabolic energy consumption of walking in the device. Oxygen consumption was measured on six able-bodied subjects performing 6 min walk tests at three different speeds (0.4, 0.8, and 1.2 m/s) under two different conditions: one with the motors producing no torque to compensate for friction, and the other having the motors injecting power to overcome passive friction based on a feedforward friction model. Average oxygen consumption in the uncompensated condition across all speeds, measured in Metabolic Equivalent of Task (METs), was statistically different than the friction compensated condition. There was an average decrease of 8.8% for METs and 1.9% for heart rate across all speeds. While oxygen consumption was reduced when the brace performed friction compensation, other factors may have a greater contribution to the metabolic energy consumption when using the device. Future studies will assess the effects of gravity compensation on the muscular effort required to lift the weight of the distal segments of the exoskeleton as well as the sagittal plane constraint on walking motions in individuals with spinal cord injuries (SCI).