Flow-pose Net: an effective two-stream network for fall detection

Fei, Kexin; Wang, Chao; Zhang, Jiaxu; Liu, Yuanzhong; Xie, Xing; Tu, Zhigang

doi:10.1007/s00371-022-02416-2

Cited by 17 publications

(2 citation statements)

References 48 publications

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“…Subsequently, the frames underwent the application of the PoseNet feature extraction technique. The PoseNet algorithm is extensively employed in the estimation of the human body pose [ 49 ]. The system examines the bodily posture in every frame and identifies significant landmarks, including joint positions, angles, and body key points.…”

Section: Materials and Methodsmentioning

confidence: 99%

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence

Siddiqui,

Saleem,

Raza

et al. 2023

Diagnostics

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A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions.

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Section: Materials and Methodsmentioning

confidence: 99%

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence

Siddiqui,

Saleem,

Raza

et al. 2023

Diagnostics

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show abstract

“…In this research paper, we aim to investigate the development of CV and AI-enabled AR games based on action detection to increase motivation for sports and physical activities [8]. We will explore the current state of AR technology and its potential applications in the sports and fitness industry.…”

Section: Introductionmentioning

confidence: 99%

Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

Doskarayev¹,

Omarov²,

Omarov³

et al. 2023

IJACSA

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This research paper presents the development of computer vision-enabled augmented reality games based on action detection to increase motivation for sports. With the increasing popularity of digital games, physical activity and sports participation have been declining, especially among the younger generation. To address this issue, we developed a series of augmented reality games that require players to perform physical actions to progress and succeed in the game. These games were developed using computer vision technology to detect the players' movements and provide real-time feedback, enhancing the gaming experience and promoting physical activity. The results of our user study showed that participants who played the augmented reality games reported higher levels of motivation to engage in physical activity and sports. The findings suggest that computer vision-enabled augmented reality games can be an effective tool to promote physical activity and sports participation, especially among younger generations.

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Short video fingerprint extraction: from audio–visual fingerprint fusion to multi-index hashing

Zhang

Wang

et al. 2022

Multimedia Systems

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Flow-pose Net: an effective two-stream network for fall detection

Cited by 17 publications

References 48 publications

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence

Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

Short video fingerprint extraction: from audio–visual fingerprint fusion to multi-index hashing

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