Human Activity Detection Employing Full-Type 2D Blazepose Estimation with LSTM

Setiyadi, Suto; Mukhtar, Husneni; Cahyadi, Willy Anugrah; Lee, Chien-Cheng; Hong, Wei-Tyng

doi:10.1109/apwimob56856.2022.10014270

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…The use of technology in promoting physical activity and sports participation has gained significant attention in recent years [30][31][32]. Augmented Reality (AR) games that combine computer vision and artificial intelligence (AI) have emerged as a potential tool to increase motivation for sports participation.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…The greater the activation within each joint, the more important that joint is in determining the type of action. We also refer to activation values as attention weights [10]. As shown in Figure 4, instead of assigning equal importance to all joints t…”

Section: Improved Lstm To Extract Spatial and Temporal Featuresmentioning

confidence: 99%

Fall detection model based on AlphaPose combined with LSTM and Lightgbm

Huang,

chen,

wang

et al. 2024

MIPPR 2023: Pattern Recognition and Computer Vision

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Major countries in the world are facing the problem of aging. Whether it is an internal cause or an external cause of the fall, if the rescue is not timely, it will cause great harm to the elderly. Therefore, we urgently need a real-time and accurate fall detection technology for timely rescue after the elderly fall. For fall detection, the existing sensor-based wearable fall detection devices are expensive to popularize, and there is a problem that the elderly forget to wear them. Therefore, a fall detection model based on AlphaPose combined with LSTM and Lightgbm is proposed. In the algorithm, AlphaPose is first used to extract the key points of the human body, and then two LSTM sub-networks are used to extract temporal and spatial features, and then sent to the main LSTM network for feature fusion. Lightgbm performs classification to achieve more accurate detection results. Experiments were conducted on two fall datasets, KFALL and UR, and the fall detection accuracy rates were 94.43% and 93.81%, respectively.

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