Influences of Muscular Fatigue on Ankle Joint Motion Recognition Based on Electromyography Signal

Al-Quraishi, Maged S.; Elamvazuthi, Irraivan; Daud, Siti Asmah; Parasuraman, S.; Borboni, Alberto

doi:10.1109/icias.2018.8540632

Cited by 2 publications

(2 citation statements)

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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%

“…These two techniques have demonstrated exceptional recognition accuracy for several common lower‐limb motions. [ 24 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] Moreover, EMG and sEMG have also been combined with inertial sensors to obtain more information. [ 34 , 35 , 36 , 37 ] sEMG, in particular, is more widely used than EMG due to its noninvasiveness and ease of use.…”

Section: Introductionmentioning

confidence: 99%

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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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