Abstract Background Few studies have systematically investigated robust controllers for lower limb rehabilitation exoskeletons (LLREs) that can safely and effectively assist users with a variety of neuromuscular disorders to walk with full autonomy. One of the key challenges for developing such a robust controller is to handle different degrees of uncertain human-exoskeleton interaction forces from the patients. Consequently, conventional walking controllers either are patient-condition specific or involve tuning of many control parameters, which could behave unreliably and even fail to maintain balance. Methods We present a novel, deep neural network, reinforcement learning-based robust controller for a LLRE based on a decoupled offline human-exoskeleton simulation training with three independent networks, which aims to provide reliable walking assistance against various and uncertain human-exoskeleton interaction forces. The exoskeleton controller is driven by a neural network control policy that acts on a stream of the LLRE’s proprioceptive signals, including joint kinematic states, and subsequently predicts real-time position control targets for the actuated joints. To handle uncertain human interaction forces, the control policy is trained intentionally with an integrated human musculoskeletal model and realistic human-exoskeleton interaction forces. Two other neural networks are connected with the control policy network to predict the interaction forces and muscle coordination. To further increase the robustness of the control policy to different human conditions, we employ domain randomization during training that includes not only randomization of exoskeleton dynamics properties but, more importantly, randomization of human muscle strength to simulate the variability of the patient’s disability. Through this decoupled deep reinforcement learning framework, the trained controller of LLREs is able to provide reliable walking assistance to patients with different degrees of neuromuscular disorders without any control parameter tuning. Results and conclusion A universal, RL-based walking controller is trained and virtually tested on a LLRE system to verify its effectiveness and robustness in assisting users with different disabilities such as passive muscles (quadriplegic), muscle weakness, or hemiplegic conditions without any control parameter tuning. Analysis of the RMSE for joint tracking, CoP-based stability, and gait symmetry shows the effectiveness of the controller. An ablation study also demonstrates the strong robustness of the control policy under large exoskeleton dynamic property ranges and various human-exoskeleton interaction forces. The decoupled network structure allows us to isolate the LLRE control policy network for testing and sim-to-real transfer since it uses only proprioception information of the LLRE (joint sensory state) as the input. Furthermore, the controller is shown to be able to handle different patient conditions without the need for patient-specific control parameter tuning.
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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).
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- Award ID(s):
- 1739800
- NSF-PAR ID:
- 10292964
- Date Published:
- Journal Name:
- Frontiers in Neurorobotics
- Volume:
- 14
- ISSN:
- 1662-5218
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)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.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fnbot.2020.588950
