Research on Human Motion Recognition Based on Lower Limb Electromyography (EMG) Signals

Wang, Junyao; Dai, Yuehong; Tong, Kang; Si, Xiaxi

doi:10.1109/icet51757.2021.9451086

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…By employing both time-domain and frequency-domain feature extraction methods, researchers aim to capture essential information from the sEMG signals and improve the accuracy and reliability of the subsequent classification and identification tasks [27][28][29][30].…”

Section: Semg Signal Feature Extractionmentioning

confidence: 99%

Positional analysis of assisting muscles for handling-assisted exoskeleton

Wang,

Guan,

et al. 2024

Preprint

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In order to better design the Handling-assisted exoskeleton, it is necessary to analyze the biomechanics of humane handling movements. In this study, Anybody Modeling System(AMS) simulation was used to analyze the movement state of muscles during humane handling. Combined with surface electromyography(sEMG) experiments, specific analysis and verification were carried out to obtain the position of muscles that the human body needs to assist during handling. In this study, the simulation and experiment were carried out for the manual handling process. The treatment group and the experimental group were set up. This study found that the vastus medialis muscle, vastus lateralis muscle, latissimus dorsi muscle, trapezius muscle, deltoid muscle and triceps brachii muscle needs more power in the process of handling, and it is reasonable and effective to combine sEMG signals with simulation of the musculoskeletal model to analyze the muscle condition of humane movement.

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Section: Semg Signal Feature Extractionmentioning

confidence: 99%

Positional analysis of assisting muscles for handling-assisted exoskeleton

Wang,

Guan,

et al. 2024

Preprint

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“…[ 22 ] Therefore, muscle activity measurement is a popular approach for capturing and recognizing human lower‐limb motions without interfering with joint movement. Presently, the standard muscle activity measurement techniques are electromyography (EMG) [ 23 , 24 ] and surface EMG (sEMG) with using noninvasive electrodes, [ 13 , 25 , 26 , 27 ] both of which measure the electrical signals generated by muscle contraction. These two techniques have demonstrated exceptional recognition accuracy for several common lower‐limb motions.…”

Section: Introductionmentioning

confidence: 99%

AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

Suo,

Liu,

Wang

et al. 2024

Advanced Science

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Motion recognition (MR)‐based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human‐computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non‐intrusive muscle‐sensing wearable device, that in conjunction with machine learning, enables motion‐control‐based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16‐channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower‐limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle‐sensing‐based somatosensory interaction, using the proposed wearable device, for enabling the real‐time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography‐based methods for achieving accurate human motion capture, which can further broaden the applications of motion‐interactive wearable devices for the coming metaverse age.

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