TWIICE — A lightweight lower-limb exoskeleton for complete paraplegics

Vouga, Tristan; Baud, Romain; Fasola, Jemina; Bouri, Mohamed; Bleuler, Hannes

doi:10.1109/icorr.2017.8009483

Cited by 43 publications

(47 citation statements)

References 17 publications

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“…Gait rehabilitation robotic exoskeletons are interfaces affixed to the body. They can provide controlled gait assistance [7], [8], [15], [16] and be used to analyse neuromechanical factors such as spasticity and voluntary muscle force level [7], or joint impedance during leg swinging [17]. However, exoskeletons constrain the joints movement, and may induce non-negligible vibrations due to the difficulty to design a rigid mechanical structure.…”

Section: A Existing Neuromechanics Estimation Devicesmentioning

confidence: 99%

Cable-Driven Robotic Interface for Lower Limb Neuromechanics Identification

Huang¹,

Farkhatdinov

Arami

et al. 2021

IEEE Trans. Biomed. Eng.

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This paper presents a versatile cable-driven robotic interface to investigate the single-joint joint neuromechanics of the hip, knee and ankle in the sagittal plane. This endpoint-based interface offers highly dynamic interaction and accurate position control (as is typically required for neuromechanics identification), and provides measurements of position, interaction force and electromyography (EMG) of leg muscles. It can be used with the subject upright, corresponding to a natural posture during walking or standing, and does not impose kinematic constraints on a joint, in contrast to existing interfaces. Mechanical evaluations demonstrated that the interface yields a rigidity above 500 N/m with low viscosity. Tests with a rigid dummy leg and linear springs show that it can identify the mechanical impedance of a limb accurately. A smooth perturbation is developed and tested with a human subject, which can be used to estimate the hip neuromechanics.

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Section: A Existing Neuromechanics Estimation Devicesmentioning

confidence: 99%

Cable-Driven Robotic Interface for Lower Limb Neuromechanics Identification

Huang¹,

Farkhatdinov

Arami

et al. 2021

IEEE Trans. Biomed. Eng.

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“…The two postural controllers have been implemented on the lower limb exoskeleton TWIICE One 2018, Figure 1D . This exoskeleton is similar to the version of 2016 introduced in (Vouga et al, 2017 ). The mechanical design and the control framework are the same, while the actuators are more compact and more powerful (Billet et al, 2019 ).…”

Section: Postural Control Frameworkmentioning

confidence: 99%

Bioinspired Postural Controllers for a Locked-Ankle Exoskeleton Targeting Complete SCI Users

et al. 2020

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Several lower-limb exoskeletons enable overcoming obstacles that would impair daily activities of wheelchair users, such as going upstairs. Still, as most of the currently commercialized exoskeletons require the use of crutches, they prevent the user from interacting efficiently with the environment. In a previous study, a bio-inspired controller was developed to allow dynamic standing balance for such exoskeletons. It was however only tested on the device without any user. This work describes and evaluates a new controller that extends this previous one with an online model compensation, and the contribution of the hip joint against strong perturbations. In addition, both controllers are tested with the exoskeleton TWIICE One, worn by a complete spinal cord injury pilot. Their performances are compared by the mean of three tasks: standing quietly, resisting external perturbations, and lifting barbells of increasing weight. The new controller exhibits a similar performance for quiet standing, longer recovery time for dynamic perturbations but better ability to sustain prolonged perturbations, and higher weightlifting capability.

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“…Due to the aging population and other factors, an increasing number of people are suffering from neurological disorders, such as stroke, central nervous system disorder, and spinal cord injury (SCI) that affect the patient’s mobility. Emerging from the field of robotics, robotic exoskeletons have become a promising solution to enable mobility impaired people to perform the activities of daily living (ADLs) ( Mergner and Lippi, 2018 ; Vouga et al, 2017 ; Zhang et al, 2018 ). Lower-limb rehabilitation exoskeletons are wearable bionic devices that are equipped with powerful actuators to assist people to regain their lower leg function and mobility.…”

Section: Introductionmentioning

confidence: 99%

“…They often offer assistance via pre-planned trajectories of gait and provide limited control to perform diverse human motions. Some well known exoskeletons include the ReWalk (ReWalk Robotics), Ekso (Ekso bionics), Indego (Parker Hannifin), TWIICE ( Vouga et al, 2017 ) and VariLeg ( Schrade et al, 2018 ). When holding the crutches, the patient’s interactions with the environment is also limited ( Baud et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning and Control of a Lower Extremity Exoskeleton for Squat Assistance

et al. 2021

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A significant challenge for the control of a robotic lower extremity rehabilitation exoskeleton is to ensure stability and robustness during programmed tasks or motions, which is crucial for the safety of the mobility-impaired user. Due to various levels of the user’s disability, the human-exoskeleton interaction forces and external perturbations are unpredictable and could vary substantially and cause conventional motion controllers to behave unreliably or the robot to fall down. In this work, we propose a new, reinforcement learning-based, motion controller for a lower extremity rehabilitation exoskeleton, aiming to perform collaborative squatting exercises with efficiency, stability, and strong robustness. Unlike most existing rehabilitation exoskeletons, our exoskeleton has ankle actuation on both sagittal and front planes and is equipped with multiple foot force sensors to estimate center of pressure (CoP), an important indicator of system balance. This proposed motion controller takes advantage of the CoP information by incorporating it in the state input of the control policy network and adding it to the reward during the learning to maintain a well balanced system state during motions. In addition, we use dynamics randomization and adversary force perturbations including large human interaction forces during the training to further improve control robustness. To evaluate the effectiveness of the learning controller, we conduct numerical experiments with different settings to demonstrate its remarkable ability on controlling the exoskeleton to repetitively perform well balanced and robust squatting motions under strong perturbations and realistic human interaction forces.

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TWIICE — A lightweight lower-limb exoskeleton for complete paraplegics

Cited by 43 publications

References 17 publications

Cable-Driven Robotic Interface for Lower Limb Neuromechanics Identification

Cable-Driven Robotic Interface for Lower Limb Neuromechanics Identification

Bioinspired Postural Controllers for a Locked-Ankle Exoskeleton Targeting Complete SCI Users

Reinforcement Learning and Control of a Lower Extremity Exoskeleton for Squat Assistance

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