Thumb-tip Force Prediction Based on Hill’s Muscle Model using Electromyogram and Ultrasound Signal

Sidek, Shahrul Na’im; Roslan, Muhammad Rozaidi; Sidek, Sabrilhakim

doi:10.2991/ijcis.11.1.18

Cited by 6 publications

(1 citation statement)

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“…Researchers have been investigating multi-sensory system to predict the muscle force during human's movement, such as walking or leg extension. Sidek et al [29] developed a thumb-tip force prediction system based on musculoskeletal human model. However, due to the muscle contraction led thickness changes is nonlinear, and the dynamic motion can further cause the changes in both of thickness and force of the muscle, it is a significant challenge to achieve the real-time muscle force estimating during a dynamic exercise [30].…”

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%