Search for: All records

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. 

The development of a hybrid system for people with spinal cord injuries is described. The system includes implanted neural stimulation to activate the user's otherwise paralyzed muscles, an exoskeleton with electromechanical actuators at the hips and knees, and a sensory and control system that integrates both components. We are using a muscle-first approach: The person's muscles are the primary motivator for his/her joints and the motors provide power assistance. This design philosophy led to the development of high efficiency, low friction joint actuators, and feed-forward, burst-torque control. The system was tested with two participants with spinal cord injury (SCI) and unique implanted stimulation systems. Torque burst addition was found to increase gait speed. The system was found to satisfy the main design requirements as laid out at the outset. 

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). 

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. 

A control system for bipedal walking in the sagittal plane was developed in simulation. The biped model was built based on anthropometric data for a 1.8 m tall male of average build. At the core of the controller is a deep deterministic policy gradient (DDPG) neural network that was trained in GAZEBO, a physics simulator, to predict the ideal foot placement to maintain stable walking despite external disturbances. The complexity of the DDPG network was decreased through carefully selected state variables and a distributed control system. Additional controllers for the hip joints during their stance phases and the ankle joint during toe-off phase help to stabilize the biped during walking. The simulated biped can walk at a steady pace of approximately 1 m/s, and during locomotion it can maintain stability with a 30 kg·m/s impulse applied forward on the torso or a 40 kg·m/s impulse applied rearward. It also maintains stable walking with a 10 kg backpack or a 25 kg front pack. The controller was trained on a 1.8 m tall model, but also stabilizes models 1.4–2.3 m tall with no changes. 

A control system for simulated two-dimensional bipedal walking was developed. The biped model was built based on anthropometric data. At the core of the control is a Deep Deterministic Policy Gradients (DDPG) neural network that is trained in GAZEBO, a physics simulator, to predict the ideal foot location to maintain stable walking under external impulse load. Additional controllers for hip joint movement during stance phase, and ankle joint torque during toeoff, help to stabilize the robot during walking. The simulated robot can walk at a steady pace of approximately 1m/s, and during locomotion it can maintain stability with a 30N-s impulse applied at the torso. This work implement DDPG algorithm to solve biped walking control problem. The complexity of DDPG network is decreased through carefully selected state variables and distributed control system. 

This paper presents the design and deployment of a modular, portable and inexpensive embedded control system architecture for the hybrid neuroprosthesis (HNP). It consist of a suite of custom designed electronic hardware and firmware to provide wireless connectivity for close-loop control with mechanical exoskeletal constraints and neural stimulation with provisions for power assist to restore locomotion functions for individuals with spinal cord injury (SCI). The design philosophy, methodology, and implementation are described and discussed in details. Bench testing and subject experimentation have been conducted to evaluate the performance of the HNP system. We conclude that the embedded control system meets the technical requirements and design criteria, and can thus be considered as a potential reference design for generic biomedical research and clinical deployment in the neuroprosthetic and exoskeleton fields.