Learning-based Walking Assistance Control Strategy for a Lower Limb Exoskeleton with Hemiplegia Patients

Huang, Rui; Pengl, Zhinan; Cheng, Hong; Hu, Jiangping; Qiu, Jing; Zou, Chaobin; Chen, Qiming

doi:10.1109/iros.2018.8594464

Cited by 20 publications

(9 citation statements)

References 19 publications

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“…One of the most common ways to ensure that is to use crutches or other balance assistance devices for additional support to avoid falling down during walking. Some commercially available exoskeletons include ReWalk (ReWalk Robotics), Ekso (Ekso bionics), Indego (Parker Hannifin), TWIICE [6], VariLeg [11] and LFMAS [12]. ReWalk measures the tilt angle of the upper body to initiate walking, and Ekso uses accelerometers on crutches and pressure sensors on shoes to detect the walking intention of the wearer.…”

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. 2021

Preprint

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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 and robust controller for a LLRE based on a decoupled deep reinforcement learning framework 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, 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 the human with different degrees of neuromuscular disorders. 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. An ablation study demonstrates 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 parameters tuning.

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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. 2021

Preprint

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“…The latter reported a strategy for cases where gait is impaired on one side of the body such as in hemiplegic people, the amputee, or those that suffered from stroke [3], and crutches or canes are also needed. The sensor readings from the unaffected leg of an exoskeleton can be used to control the side of the affected leg as reported in [22]. In these cases, since only one side is affected, a unilateral aid can be used, such as a single leg exoskeleton.…”

Section: Introductionmentioning

confidence: 99%

Generation of Gait Events with a FSR Based Cane Handle

Trujillo-León

Guzmán-Manzano

Velázquez

et al. 2021

Sensors

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Gait analysis has many applications, and specifically can improve the control of prosthesis, exoskeletons, or Functional Electrical Stimulation systems. The use of canes is common to complement the assistance in these cases, and the synergy between upper and lower limbs can be exploited to obtain information about the gait. This is interesting especially in the case of unilateral assistance, for instance in the case of one side lower limb exoskeletons. If the cane is instrumented, it can hold sensors that otherwise should be attached to the body of the impaired user. This can ease the use of the assistive system in daily life as well as its acceptance. Moreover, Force Sensing Resistors (FSRs) are common in gait phase detection systems, and force sensors are also common in user intention detection. Therefore, a cane that incorporates FSRs on the handle can take advantage from the direct interface with the human and provide valuable information to implement real-time control. This is done in this paper, and the results confirm that many events are detected from variables derived from the readings of the FSRs that provide rich information about gait. However, a large inter-subject variability points to the need of tailored control systems.

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“…The Chinese University of Science and Technology designed an exoskeleton robot driven by a servo motor and developed a fuzzy algorithm for lower extremity exoskeleton (Huang et al, 2016a). The exoskeleton team of H. C. from University of Electronic Science and Technology of China have developed exoskeletons for paraplegia (Huang et al, 2018), hemiplegia patients (Peng et al, 2020), and human-power augmentation (Huang et al, 2016b(Huang et al, , 2019. The exoskeleton group at the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences has developed the fourth generation of the SIAT exoskeleton robot.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons

et al. 2021

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Herein, we propose a real-time stable control gait switching method for the exoskeleton rehabilitation robot. Exoskeleton rehabilitation robots have been extensively developed during the past decade and are able to offer valuable motor ability to paraplegics. However, achieving stable states of the human-exoskeleton system while conserving wearer strength remains challenging. The constant switching of gaits during walking may affect the center of gravity, resulting in imbalance of human–exoskeleton system. In this study, it was determined that forming an equilateral triangle with two crutch-supporting points and a supporting leg has a positive impact on walking stability and ergonomic interaction. First, the gaits planning and stability analysis based on human kinematics model and zero moment point method for the lower limb exoskeleton are demonstrated. Second, a neural interface based on surface electromyography (sEMG), which realizes the intention recognition and muscle fatigue estimation, is constructed. Third, the stability of human–exoskeleton system and ergonomic effects are tested through different gaits with planned and unplanned gait switching strategy on the SIAT lower limb rehabilitation exoskeleton. The intention recognition based on long short-term memory (LSTM) model can achieve an accuracy of nearly 99%. The experimental results verified the feasibility and efficiency of the proposed gait switching method for enhancing stability and ergonomic effects of lower limb rehabilitation exoskeleton.

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Learning-based Walking Assistance Control Strategy for a Lower Limb Exoskeleton with Hemiplegia Patients

Cited by 20 publications

References 19 publications

Robust Walking Control of a Lower Limb Rehabilitation Exoskeleton Coupled with a Musculoskeletal Model via Deep Reinforcement Learning

Robust Walking Control of a Lower Limb Rehabilitation Exoskeleton Coupled with a Musculoskeletal Model via Deep Reinforcement Learning

Generation of Gait Events with a FSR Based Cane Handle

A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons

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