Dynamics Combined With Hill Model for Functional Electrical Stimulation Ankle Angle Prediction

Zhang, Xianghong; Jiang, Ziqin; Li, Xu; Xu, Pan; Vasić, Željka Lučev; Čuljak, Ivana; Cifrek, Mario; Du, Min; Gao, Yueming

doi:10.1109/jbhi.2022.3158426

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Among different estimation modalities, surface electromyography (sEMG) and musculoskeletal ultrasound are promising technologies because of their reliability and accuracy in reflecting muscle activities. sEMG has been widely utilized due to its advantages of desirable classification accuracy and feasibility in pattern recognition (PR) [5][6][7], muscle fatigue evaluation [8], and muscle contraction strength [9][10][11]. sEMG can only detect the motion of superficial muscles but cannot recognize bioelectric signals from deep soft tissues, thereby limiting its accuracy in estimating muscle force for some bulk muscles, such as rectus femoris and applying for patients with muscle weakness [12,13].…”

Section: Introductionmentioning

confidence: 99%

Investigation of a wearable piezoelectric-IMU multi-modal sensing system for real-time muscle force estimation

Lu¹,

Cao²,

Chen³

et al. 2023

Smart Mater. Struct.

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Piezoelectric based ultrasonic transducer shows a promising application prospect in the wearable muscle force estimator by detecting the morphological-biochemical peculiarity of human motion. However, due to the nonlinearity of muscle contraction, muscle force estimation in a dynamic motion, such as leg lifting, is still a challenge. In this study, a wearable multi-sensory system was developed for muscle force estimation in the isometric contraction assessment and during the dynamic training. A customized wearable ultrasound system was adopted for real-time deformation measurement of muscle, and an IMU sensor was utilized to detect the joint angle. Thus, the muscle force can be predicted by identifying the muscle deformation as well as considering the muscle thickness change caused by the joint angel variation. The robustness and efficiency of the system was investigated by evaluating the muscle force of the rectus femoris during the isometric contraction assessment and the knee’s dynamic exercise. The accuracy of muscle force prediction is over 90%. During the knee’s dynamic exercise, the predicted force output of the lower-limb agreed well with the measured value, demonstrating the promising application of the system in dynamic muscle force estimation. This approach can provide real-time muscle force information for the patients to improve the rehabilitative training effect when using an exoskeletal rehabilitation robot as well as evaluate their recovery situation.

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Section: Introductionmentioning

confidence: 99%

Investigation of a wearable piezoelectric-IMU multi-modal sensing system for real-time muscle force estimation

Lu¹,

Cao²,

Chen³

et al. 2023

Smart Mater. Struct.

View full text Add to dashboard Cite

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A Block-Wised and Sparsely-Connected ANN for the Prediction of Human Joint Moment Based on the Improved Hill Musculoskeletal Model

Xiong,

Zeng,

Gan

et al. 2024

International Journal of Swarm Intelligence Research

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Human joint moment plays an important role in rehabilitation assessment and human-robot interaction, which cannot be measured directly but can only be predicted via indirect measurement by an artificial neural network (ANN). However, most existing ANN models for human joint moment prediction use fully-connected network which has complex structure and no inclusion of domain knowledge. Thus, this study introduced a novel block-wised and sparsely-connected ANN model (BSANN) for human joint moment prediction, which significantly reduced the computational and storage costs. In this BSANN model, by using an improved Hill musculoskeletal (HMS) model, a single-output fully-connected network was established as a block to take each electromyograph (EMG) signal for the prediction of the muscle moment, and all muscle moments were connected together as inputs to obtain the joint moment. Compared to the ANN, our BSANN model decreased 80.7% connections and keeps good prediction accuracy. It provides embedded portable systems a powerful tool to predict joint moment.

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Dynamics Combined With Hill Model for Functional Electrical Stimulation Ankle Angle Prediction

Cited by 2 publications

References 27 publications

Investigation of a wearable piezoelectric-IMU multi-modal sensing system for real-time muscle force estimation

Investigation of a wearable piezoelectric-IMU multi-modal sensing system for real-time muscle force estimation

A Block-Wised and Sparsely-Connected ANN for the Prediction of Human Joint Moment Based on the Improved Hill Musculoskeletal Model

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