Thigh Skin Strain Model for Human Gait Movement

Liu, Hongcheng; Zhang, Xiaodong; Zhu, Ke; Niu, Hang

doi:10.1109/acie51979.2021.9381089

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Muscles can also be monitored according to morphological changes in muscle shape during muscle contraction (that is, muscle fibers shorten in length and increase in circumference to maintain volume). [ 60 ] One study reported a fiber strain sensor for estimating the muscle force through muscle circumference measurements. [ 59 ] Researchers have also applied capacitive sensors to measure muscle shape changes using the human body as a dielectric layer.…”

Section: Discussionmentioning

confidence: 99%

“…Contraction of the calf muscles results in deforming the calf shape, keeping its volume almost unchanged. [ 60 ] The resulting deformation of skin surface contours can be measured using wearable soft sensors. Moreover, the vibrational MMG signals induced by muscle fiber oscillations propagate to the skin surface and can be measured simultaneously.…”

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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Section: Discussionmentioning

confidence: 99%

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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“…[ 35 , 36 ] abstracted various parts of the human body into a stick model to simulate the movement characteristics of the human body while walking. In [ 37 ], the rotation angles of the hip and knee joints were used to characterise the gait when a person walks. Ref.…”

Section: Related Workmentioning

confidence: 99%

Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction

Han,

2023

Sensors

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As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial–temporal graph convolutional networks’ bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust.

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Thigh Skin Strain Model for Human Gait Movement

Cited by 2 publications

References 9 publications

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

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

Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction

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