A Review of Research on Human Behavior Recognition Methods Based on Deep Learning

Li, Shengwang; Yu, Pengshuai; Xu, Yuhua; Zhang, Jingwei

doi:10.1109/icrcv55858.2022.9953244

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Dataset Accuracy ST-LSTM [11] Kinetics 88.4 ST-RNN [13] Kinetics 87.6 CNN [15] Kinetics 74.2 GCN [18] Kinetics 85.3 ST-GCN [20] Kinetics 93.2 2s-AGCN [23] Kinetics 95.5 T-Conv [24] Kinetics 79.3 AS-GCN [26] Kinetics 91.2 Nevertheless, while the aforementioned methods pay attention to the spatial connection of bone point data, they disregard the detailed information in the action process, the recognition accuracy is low, and they are not well suited for the recognition of complex tennis movements [32][33][34][35][36].…”

Section: Methodsmentioning

confidence: 99%

A Tennis Training Action Analysis Model Based on Graph Convolutional Neural Network

Zhang,

Chen

2023

IEEE Access

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With the development of artificial intelligence technology, an increasing number of human action recognition (HAR) methods are being applied to tennis training action analysis. The HAR methods based on skeletal points have been extensively researched and applied due to their superior action expression capabilities. In order to enhance the HAR ability of tennis players and effectively capture the detailed features in training actions, this paper proposes a tennis training action analysis model based on graph convolutional neural networks. Firstly, this paper establishes the limb vectors of humans in threedimensional spatial coordinates and extracts the features of tennis error techniques based on the distances between the skeletal joints of five parts of the human body. Secondly, the data's time frames are segmented to extract attention and improve the model's ability to capture detailed features. Additionally, the attention mechanism is introduced to embed the position information into the attention map, enhancing the model's generalization ability. Experiments conducted on several action datasets demonstrate that the proposed model in this paper achieves higher HAR accuracy and better recognition results compared to most current methods.

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Section: Methodsmentioning

confidence: 99%

A Tennis Training Action Analysis Model Based on Graph Convolutional Neural Network

Zhang,

Chen

2023

IEEE Access

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A Review of Recent Techniques for Human Activity Recognition: Multimodality, Reinforcement Learning, and Language Models

Oleh,

Obermaisser,

Ahammed

2024

Algorithms

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Human Activity Recognition (HAR) is a rapidly evolving field with the potential to revolutionise how we monitor and understand human behaviour. This survey paper provides a comprehensive overview of the state-of-the-art in HAR, specifically focusing on recent techniques such as multimodal techniques, Deep Reinforcement Learning and large language models. It explores the diverse range of human activities and the sensor technologies employed for data collection. It then reviews novel algorithms used for Human Activity Recognition with emphasis on multimodality, Deep Reinforcement Learning and large language models. It gives an overview of multimodal datasets with physiological data. It also delves into the applications of HAR in healthcare. Additionally, the survey discusses the challenges and future directions in this exciting field, highlighting the need for continued research and development to fully realise the potential of HAR in various real-world applications.

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A Review of Research on Human Behavior Recognition Methods Based on Deep Learning

Cited by 2 publications

References 20 publications

A Tennis Training Action Analysis Model Based on Graph Convolutional Neural Network

A Tennis Training Action Analysis Model Based on Graph Convolutional Neural Network

A Review of Recent Techniques for Human Activity Recognition: Multimodality, Reinforcement Learning, and Language Models

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