Muscle Strength Assessment System Using sEMG-Based Force Prediction Method for Wrist Joint

Zhang, Songyuan; Guo, Shuxiang; Gao, Baofeng; Huang, Qiang; Pang, Muye; Hirata, Hideyuki; Ishihara, Hidenori

doi:10.1007/s40846-016-0112-5

Cited by 49 publications

(23 citation statements)

References 30 publications

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“…Here, the threshold is determined by the flow theory [31] with which patients can achieve an optimal training experience to keep the balance between training difficulty level and patient status. In our previous research, a muscle strength assessment system, which records the activation level of skeleton muscles, was proposed [19], with which the patient can be trained within the range of optimal training, where the difficulty level of training is in accordance with his/her muscle strength. When the training difficulty level exceeds patients’ ability, overtraining will happen; on the contrary, undertraining will happen when the opposite occurs.…”

Section: Methodsmentioning

confidence: 99%

“…Patients can perform a tracking task in a virtual environment with coordination training of bilateral upper extremity through a haptic device and an inertia sensor [17]. Our previous research proposed an upper limb elbow joint representation method that uses only a one-channel electromyography (EMG) signal implemented for bilateral neuro-rehabilitation [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Coordinative Motion-Based Bilateral Rehabilitation Training System with Exoskeleton and Haptic Devices for Biomedical Application

Zhang

Guo

et al. 2018

Micromachines

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According to the neuro-rehabilitation theory, compared with unilateral training, bilateral training is proven to be an effective method for hemiparesis, which affects the most part of stroke patients. In this study, a novel bilateral rehabilitation training system, which incorporates a lightweight exoskeleton device worn on the affected limb; a haptic device (Phantom Premium), which is used for generating a desired tactile feedback for the affected limb; and a VR (virtual reality) graphic interface, has been developed. The use of VR technology during rehabilitation can provide goal directed tasks with rewards and motivate the patient to undertake extended rehabilitation. This paper is mainly focused on elbow joint training, and other independent joints can be trained by easily changing the VR training interface. The haptic device is adopted to enable patients to use their own decision making abilities with a tactical feedback. Integrated with a VR-based graphic interface, the goal-oriented task can help to gradually recovery their motor function with a coordinative motion between two limbs. In particular, the proposed system can accelerate neural plasticity and motor recovery in those patients with little muscle strength by using the exoskeleton device. The exoskeleton device can provide from relatively high joint impedance to near-zero impedance, and can provide a partial assist as the patient requires.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coordinative Motion-Based Bilateral Rehabilitation Training System with Exoskeleton and Haptic Devices for Biomedical Application

Zhang

Guo

et al. 2018

Micromachines

Self Cite

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“…GRNN is a special RBF neural network with a radial basis network layer and a special linear network layer, which has a good local approximation property and is commonly used in function approximation. The threshold of the radial basis network layer b is affected by the RBF smoothing parameter spread, as shown in (7).…”

Section: Generalized Regression Neural Network Based On Golden-secmentioning

confidence: 99%

“…M. Pang et al [6] presented a Hill-type-based muscular model and a state switching model for the continuous estimation of elbow joint angle, and the predicted results were used to control an exoskeleton device. S. Zhang et al [7] proposed a forearm muscle strength estimation method based on musculoskeletal model with Bayesian linear regression algorithm for calibrating parameters. Although, kinetic model can well explain the process of motion generation, it requires more human parameters to be measured and some of these parameters cannot be directly measured, which leads to a complex modeling process.…”

Section: Introductionmentioning

confidence: 99%

Knee Joint Angle Prediction Based on Muscle Synergy Theory and Generalized Regression Neural Network

Liu

et al. 2018

2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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Continuous joint motion estimation plays an important part in accomplishing more compliant and safer human-machine interaction (HMI). Surface electromyogram (sEMG) signals, which contain abundant motion information, can be used as a source for continuous joint motion estimation. In this paper, a knee joint angle prediction system based on muscle synergy theory and generalized regression neural network (GRNN) was proposed. The wavelet transform threshold method was used for sEMG signals and angle trajectories denoising. The time-domain features wave-length extracted from four-channel sEMG signals were decomposed into a synergy matrix and an activation coefficient matrix by using nonnegative matrix factorization based on muscle synergy theory. A GRNN based on golden-section search was employed to build the activation model mapping from the activation coefficients to the knee joint angles, so as to realize the continuous knee joint angle estimation. The experimental results show that the average coefficient of determination is 0.933. In addition, a user graphic interface based on the Java platform was designed to display the dynamic sEMG data and predicted knee joint angles in real time.

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“…When a gesture is performed, the subject would provide different force levels according to the needs of the environment. As discussed in [19][20][21], these levels can also be decoded by analyzing the EMG signals. This naturally motivates us to explore the multi-task learning (MTL) framework to decode gestures and force levels in sEMG signals [22].…”

Section: Introductionmentioning

confidence: 99%

A force levels and gestures integrated multi-task strategy for neural decoding

Hua

Wang

Xie

et al. 2020

Complex Intell. Syst.

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This paper discusses the problem of decoding gestures represented by surface electromyography (sEMG) signals in the presence of variable force levels. It is an attempt that multi-task learning (MTL) is proposed to recognize gestures and force levels synchronously. First, methods of gesture recognition with different force levels are investigated. Then, MTL framework is presented to improve the gesture recognition performance and give information about force levels. Last but not least, to solve the problem that using the greedy principle in MTL, a modified pseudo-task augmentation (PTA) trajectory is introduced. Experiments conducted on two representative datasets demonstrate that compared with other methods, frequency domain information with convolutional neural network (CNN) is more suitable for gesture recognition with variable force levels. Besides, the feasibility of extracting features that are closely related to both gestures and force levels is verified via MTL. By influencing learning dynamics, the proposed PTA method can improve the results of all tasks, and make it applicable to the case where the main tasks and auxiliary tasks are clear.

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Muscle Strength Assessment System Using sEMG-Based Force Prediction Method for Wrist Joint

Cited by 49 publications

References 30 publications

Coordinative Motion-Based Bilateral Rehabilitation Training System with Exoskeleton and Haptic Devices for Biomedical Application

Coordinative Motion-Based Bilateral Rehabilitation Training System with Exoskeleton and Haptic Devices for Biomedical Application

Knee Joint Angle Prediction Based on Muscle Synergy Theory and Generalized Regression Neural Network

A force levels and gestures integrated multi-task strategy for neural decoding

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