Prediction of hand grip strength based on surface electromyographic signals

Lv, Jidong; Yang, Yang; Niu, Lei; Sun, Xiaoqin; Wang, Lingyun; Lin, Wei; Rong, Hailong; Zou, Ling

doi:10.1016/j.jksuci.2023.04.001

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…David G. Lloyd and Thor F. Besier used sEMG signals to obtain muscle activation and calculated muscle force by combining the modified Hill model with activation and tendon length [ 20 ]. Based on these correlations, there has been considerable research on force prediction using sEMG in recent years, with studies utilizing sEMG signals to predict grip force [ 21 ] and grasping force [ 22 ], which are applied in rehabilitation process assessment and fine control of prosthetics. Grip force measurement is a straightforward, easy, and non-invasive method of assessing overall muscle force.…”

Section: Introductionmentioning

confidence: 99%

A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring

Wu,

Tian,

Zhang

et al. 2024

Sensors

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Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R2 of 0.9215, an MAE of 1.0637, and an MSE of 1.7479. The proposed system demonstrates excellent performance in real-time signal acquisition and grip force prediction, proving to be an effective muscle status monitoring tool for rehabilitation, training, disease condition surveillance and scientific fitness applications.

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Section: Introductionmentioning

confidence: 99%

A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring

Wu,

Tian,

Zhang

et al. 2024

Sensors

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Permanent magnet synchronous motor demagnetization fault diagnosis based on PCA-ISSA-PNN

Yu,

Li,

Zeng

et al. 2024

Sci Rep

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Aiming at the demagnetization fault problem of the permanent magnet synchronous motor (PMSM), a demagnetization fault diagnosis method based on the combination of the principal component analysis (PCA) algorithm, the improved sparrow search algorithm (ISSA), and the probabilistic neural network (PNN) algorithm is proposed. First, the principal components of phase currents are extracted using PCA. Second, ISSA is used to optimize the smoothing coefficients of the PNN algorithm, and the optimized PNN algorithm is combined with PCA to obtain the PCA-ISSA-PNN fault diagnosis model. Finally, the established fault diagnosis model was tested using the current data collected from the experiments and compared with the fault diagnosis indexes and optimization performance of the conventional PNN, PCA-PNN, PCA-GA (genetic algorithm)-PNN, PCA-DA (dragonfly algorithm)-PNN, PCA-GTO (artificial gorilla troop optimizer)-PNN, PCA-AHA-PNN, and PCA-SSA-PNN. The test results show that the fault diagnosis accuracy of PCA-ISSA-PNN reaches 95.83%, and the fault diagnosis indexes are significantly higher than those of PNN, PCA-PNN, PCA-GA-PNN, and PCA-DA-PNN; its optimization performance is also significantly better than that of PCA-GTO-PNN, PCA-AHA-PNN, and PCA-SSA-PNN, which verifies the accuracy and efficiency of the proposed method.

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sEMG-Controlled Soft Exo-Glove for Assistive Rehabilitation Therapies

Copaci,

Cerro,

Guadalupe

et al. 2024

IEEE Access

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The movement of the human hand offers various degrees of freedom, enabling efficient performance of dynamic tasks and robust interaction with the environment in a compliant and continuous manner. However, the rigid exoskeleton used in hand rehabilitation limits the user's freedom of movement, complicating their natural interaction with the environment. In this study, we present a soft exo-glove for assistive rehabilitation actuated by Shape Memory Alloys (SMA), controlled by a surface electromyography (sEMG) hand gesture classifier. Thanks to the actuator type, the soft exo-glove enables slow, smooth, and controlled movements when activated and provides complete control transparency when the device is not active. This advantage enhances the comfort and acceptance of the exo-glove by the patient. On the other hand, the classifier, in conjunction with the control algorithm and the soft exo-glove, offers the potential to use the exo-glove in assistive hand rehabilitation therapy. For user-friendly use, an interface has been developed, enabling the acquisition of new sEMG data from new users, retraining of the classifier, and connection with the soft exo-glove for rehabilitation therapy. The main objective of this study is to demonstrate that the proposed wearable soft exo-glove, along with the control algorithm and the employed classifier, constitutes an effective solution for assistive rehabilitation tasks, as demonstrated with healthy subjects. Furthermore, this solution can be easily adapted to the users' characteristics and requirements.

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Prediction of hand grip strength based on surface electromyographic signals

Cited by 5 publications

References 40 publications

A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring

A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring

Permanent magnet synchronous motor demagnetization fault diagnosis based on PCA-ISSA-PNN

sEMG-Controlled Soft Exo-Glove for Assistive Rehabilitation Therapies

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