A Novel User Control for Lower Extremity Rehabilitation Exoskeletons

Karunakaran, Kiran K.; Abbruzzese, Kevin; Androwis, Ghaith J.; Foulds, Richard

doi:10.3389/frobt.2020.00108

Cited by 10 publications

(5 citation statements)

References 45 publications

(68 reference statements)

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“…An equivalent method for position-control also exists [ 270 ]. In this case, a pole is linking each foot the ipsilateral hand, with a multi-axis force sensor.…”

Section: Assistive Strategiesmentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

164

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Background Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user. Methods In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user’s intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller. Results 291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user’s movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper. Conclusions By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

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“…An equivalent method for position-control also exists [ 270 ]. In this case, a pole is linking each foot the ipsilateral hand, with a multi-axis force sensor.…”

Section: Assistive Strategiesmentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

164

View full text Add to dashboard Cite

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“…Seven healthy subjects (HS group) and 2 spinal cord injury patients (SC group) were recruited to participate in this study. The HS group was comprised by 3 females and 4 males aged between 20 and 25 years old (22,20,22,21,23,22 and 25 years for HS1 to HS7, respectively), while the SC group was comprised by 2 males (patient SC1, 23 years old, 8 months since SCI event, right-handed, C5 lesion and ASIA A score, and patient SC2, 41 years old, 7 months since SCI event, right-handed, C6 lesion and ASIA A score). All participants were right-handed, with normal or corrected vision, and did not had any previous experience with EEG recordings or BCI-related experiments.…”

Section: ) Participantsmentioning

confidence: 99%

Brain-Computer Interface Controlled Functional Electrical Stimulation: Evaluation With Healthy Subjects and Spinal Cord Injury Patients

et al. 2022

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“…Because of varied conditions of patients’ disabilities, the human-exoskeleton interaction forces are unpredictable and could vary substantially from one patient to another, a very important factor to consider for controller development. Existing controllers for LLREs often focus on trajectory tracking, conventional Proportional-Integral-Derivative (PID) control [ 20 ], fuzzy control [ 8 ], model-based predictive control [ 21 ], impedance control [ 22 , 23 ], and momentum-based control [ 24 ]. The trajectory tracking approaches are primarily used for early-stage rehabilitation when patients have very weak muscle strength, its robustness against unexpected large perturbations or uncertain interaction forces is not great.…”

Section: Introductionmentioning

confidence: 99%

Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning

Luo

Androwis

Adamovich

et al. 2023

J NeuroEngineering Rehabil

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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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A Novel User Control for Lower Extremity Rehabilitation Exoskeletons

Cited by 10 publications

References 45 publications

Review of control strategies for lower-limb exoskeletons to assist gait

Review of control strategies for lower-limb exoskeletons to assist gait

Brain-Computer Interface Controlled Functional Electrical Stimulation: Evaluation With Healthy Subjects and Spinal Cord Injury Patients

Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning

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