Development of an EMG-Controlled Knee Exoskeleton to Assist Home Rehabilitation in a Game Context

Lyu, Mingxing; Chen, Wei‐Hai; Ding, Xilun; Wang, Jianhua; Pei, Zhongcai; Zhang, Baochang

doi:10.3389/fnbot.2019.00067

Cited by 69 publications

(41 citation statements)

References 52 publications

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“…1 b). The load cell is rated for a maximum force up to 50 N and has been calibrated with forces up to 30 N in both directions (extension and flexion) with a resolution of 0.0073 N. The Myo armband (Thalmic Labs) is used to record sEMG signals, which can be used in parallel with the admittance controller to trigger proportional mechanical support similar to [ 75 , 76 ]. Finally, an IMU (MPU 6050) is located on the forearm module to evaluate the spatial orientation of the eWrist , which is required to adapt the mechanical support if the user is moving [ 77 ].…”

Section: Methodsmentioning

confidence: 99%

Characterization and wearability evaluation of a fully portable wrist exoskeleton for unsupervised training after stroke

Lambelet

Temiraliuly

Siegenthaler

et al. 2020

J NeuroEngineering Rehabil

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Background Chronic hand and wrist impairment are frequently present following stroke and severely limit independence in everyday life. The wrist orientates and stabilizes the hand before and during grasping, and is therefore of critical importance in activities of daily living (ADL). To improve rehabilitation outcomes, classical therapy could be supplemented by novel therapies that can be applied in unsupervised settings. This would enable more distributed practice and could potentially increase overall training dose. Robotic technology offers new possibilities to address this challenge, but it is critical that devices for independent training are easy and appealing to use. Here, we present the development, characterization and wearability evaluation of a fully portable exoskeleton for active wrist extension/flexion support in stroke rehabilitation. Methods First we defined the requirements, and based on these, constructed the exoskeleton. We then characterized the device with standardized haptic and human-robot interaction metrics. The exoskeleton is composed of two modules placed on the forearm/hand and the upper arm. These modules weigh 238 g and 224 g, respectively. The forearm module actively supports wrist extension and flexion with a torque up to 3.7 Nm and an angular velocity up to 530 deg/s over a range of 154∘. The upper arm module includes the control electronics and battery, which can power the device for about 125 min in normal use. Special emphasis was put on independent donning and doffing of the device, which was tested via a wearability evaluation in 15 healthy participants and 2 stroke survivors using both qualitative and quantitative methods. Results All participants were able to independently don and doff the device after only 4 practice trials. For healthy participants the donning and doffing process took 61 ±15 s and 24 ±6 s, respectively. The two stroke survivors donned and doffed the exoskeleton in 54 s/22 s and 113 s/32 s, respectively. Usability questionnaires revealed that despite minor difficulties, all participants were positive regarding the device. Conclusions This study describes an actuated wrist exoskeleton which weighs less than 500 g, and which is easy and fast to don and doff with one hand. Our design has put special emphasis on the donning aspect of robotic devices which constitutes the first barrier a user will face in unsupervised settings. The proposed device is a first and intermediate step towards wearable rehabilitation technologies that can be used independently by the patient and in unsupervised settings.

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Section: Methodsmentioning

confidence: 99%

Characterization and wearability evaluation of a fully portable wrist exoskeleton for unsupervised training after stroke

Lambelet

Temiraliuly

Siegenthaler

et al. 2020

J NeuroEngineering Rehabil

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“…The methods belonging to the first category The associate editor coordinating the review of this manuscript and approving it for publication was Kan Liu . detect the wearer's walking intention by measuring biological signals of the wearer. For example, the exoskeleton named HAL (Hybrid Assistive Leg) [5], [6] and the exoskeleton developed in [7] measured the wearer's electromyography (EMG) signals for intention detection; Gui et al [8] proposed an adaptive method to estimate active joint torque by EMG signals. Since EMG signals come before contraction and relaxation of muscles, they can be used to predict the wearer's intention of motion.…”

Section: Introductionmentioning

confidence: 99%

Admittance Control of Powered Exoskeletons Based on Joint Torque Estimation

Liang

Hsiao

2020

IEEE Access

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In recent years, exoskeletons have been widely accepted as rehabilitative and walking assistive devices for either healthy persons or patients with mobility impairment. Depending on mobility of the wearers, the control strategies for exoskeletons may differ significantly. This paper aims at developing an admittance control framework for exoskeletons to assist healthy persons in safer, faster, or more energyefficient walking. A crucial step to accomplish assistive walking for healthy persons is to detect their intention. Direct sensing of the wearer's biological signals, such as electromyography (EMG) and electroencephalography (EEG), requires additional sensors, which increases the cost and makes it inconvenient for the wearer to put on and take off the exoskeleton. Instead, we propose to detect the wearer's intention by estimating the total torques applied from the wearer to the human-exoskeleton system based on the motor current and joint angles. Then the velocity commands to the joint motors are generated according to the estimated torque and a predefined mechanical admittance function of each joint. Consequently, the exoskeleton complies with the wearer's intention. Rigorous theoretical analysis is performed on robust stability of the closed-loop system. Then experiments are carried out to verify the accuracy and robustness of the proposed torque estimation algorithm. In addition, experimental data show that the wearer's gait can be shaped in a desired way by choosing an appropriate admittance function in the proposed admittance control loop.

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“…There are other works that focus on the study of injuries from other areas such as the ankle [7][8][9]. Other works focused on one of the Sensors 2020, 20, 858 3 of 17 most important joints of the lower extremities such as the knee [10][11][12][13][14][15]. In all these works, the use of robotic exoskeleton systems is another part of the traditional physiotherapeutic rehabilitation that is advised by an expert and is performed in a traditional clinical setting.…”

Section: Introductionmentioning

confidence: 99%

Connected Elbow Exoskeleton System for Rehabilitation Training Based on Virtual Reality and Context-Aware

Iglesia

Mendes

González

et al. 2020

Sensors

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Traditional physiotherapy rehabilitation systems are evolving into more advanced systems based on exoskeleton systems and Virtual Reality (VR) environments that enhance and improve rehabilitation techniques and physical exercise. In addition, due to current connected systems and paradigms such as the Internet of Things (IoT) or Ambient Intelligent (AmI) systems, it is possible to design and develop advanced, effective, and low-cost medical tools that patients may have in their homes. This article presents a low-cost exoskeleton for the elbow that is connected to a Context-Aware architecture and thanks to a VR system the patient can perform rehabilitation exercises in an interactive way. The integration of virtual reality technology in rehabilitation exercises provides an intensive, repetitive and task-oriented capacity to improve patient motivation and reduce work on medical professionals. One of the system highlights is the intelligent ability to generate new exercises, monitor the exercises performed by users in search of progress or possible problems and the dynamic modification of the exercises characteristics. The platform also allows the incorporation of commercial medical sensors capable of collecting valuable information for greater accuracy in the diagnosis and evolution of patients. A case study with real patients with promising results has been carried out.

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Development of an EMG-Controlled Knee Exoskeleton to Assist Home Rehabilitation in a Game Context

Cited by 69 publications

References 52 publications

Characterization and wearability evaluation of a fully portable wrist exoskeleton for unsupervised training after stroke

Characterization and wearability evaluation of a fully portable wrist exoskeleton for unsupervised training after stroke

Admittance Control of Powered Exoskeletons Based on Joint Torque Estimation

Connected Elbow Exoskeleton System for Rehabilitation Training Based on Virtual Reality and Context-Aware

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