Periodic Physical Activity Information Segmentation, Counting and Recognition From Video

Cheng, Sheng-Hsien; Sarwar, M.; Daraghmi, Yousef‐Awwad; İk, Tsì-Uí; Li, Yih-Lang

doi:10.1109/access.2023.3247583

Cited by 6 publications

(2 citation statements)

References 63 publications

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“…In order to enhance the capacity of the 3BGRU model to make generalizations, they used a data augmentation technique to ensure a balanced training dataset and attained the highest level of accuracy of 88%. Cheng et al [ 32 ] proposed a system for recognizing physical exercise from video frames (extracted 3D human skeleton data using VIBE) using deep semantic features and repetitive segmentation algorithms. The system locates and segments the activity into multiple unit actions, improving recognition and time intervals.…”

Section: Literature Reviewmentioning

confidence: 99%

PAR-Net: An Enhanced Dual-Stream CNN–ESN Architecture for Human Physical Activity Recognition

Khan,

Lee

2024

Sensors

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Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques are unable to fully comprehend complex activity patterns over different periods, emphasizing the need for enhanced architectures to further increase accuracy by learning spatiotemporal dependencies in the data individually. Therefore, in this work, we develop an attention-enhanced dual-stream network (PAR-Net) for physical activity recognition with the ability to extract both spatial and temporal features simultaneously. The PAR-Net integrates convolutional neural networks (CNNs) and echo state networks (ESNs), followed by a self-attention mechanism for optimal feature selection. The dual-stream feature extraction mechanism enables the PAR-Net to learn spatiotemporal dependencies from actual data. Furthermore, the incorporation of a self-attention mechanism makes a substantial contribution by facilitating targeted attention on significant features, hence enhancing the identification of nuanced activity patterns. The PAR-Net was evaluated on two benchmark physical activity recognition datasets and achieved higher performance by surpassing the baselines comparatively. Additionally, a thorough ablation study was conducted to determine the best optimal model for human physical activity recognition.

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Section: Literature Reviewmentioning

confidence: 99%

PAR-Net: An Enhanced Dual-Stream CNN–ESN Architecture for Human Physical Activity Recognition

Khan,

Lee

2024

Sensors

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“…SAR-based applications have been extensively utilized by sports analysts and coaches to enhance athletes' performance. The main objective of SAR applications is to identify the athlete's actions from an unknown video sequence, determine the action's duration and type, monitor a player's performance, track their movements, recognize the performed action, compare various actions, compare different kinds and skills of performances, or perform automatic statistical analysis [7,8,9]. Badminton is a highly technical sport that can greatly benefit from SAR-based applications for analyzing player actions.…”

Section: Introductionmentioning

confidence: 99%

Skeleton Based Keyframe Detection Framework for Sports Action Analysis: Badminton Smash Case

Sarwar,

Lin,

Daraghmi

et al. 2023

IEEE Access

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The analysis of badminton player actions from videos plays a crucial role in improving athletes' performance and generating statistical insights. The complexity and speed of badminton movements pose unique challenges compared to everyday activities. To analyze badminton player actions, we propose a skeleton-based keyframe detection framework for action analysis. Keyframe detection is widely used in video summarization and visual localization due to its computational efficiency and memory optimization compared to analyzing all frames of a video. This framework segments the complex macro-level activity into micro-level segments and analyzes each micro-level activity individually. Firstly, it extracts skeleton data from a motion sequence video using 3D:VIBE pose estimation. Then, the keyframe detection module explores the sequence of activity frames and identifies keyframes for each micro-level activity, including start, ready, strike, and end. Finally, the posture and movement detection modules analyze the posture and movement data to identify specific activities. This framework is implemented in the device called CoachBox. The proposed framework is evaluated using the mean absolute error on a dataset. The average mean absolute error for the keyframe detection module is less than 0.168 seconds, and the striking moment detection has an error of only 0.033 seconds. Additionally, a coordinate transform method is provided to convert body coordinates to real-world coordinates for visualization purposes.

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Privacy-Preserving Fall Detection Using an Ultra-Wideband Sensor With Continuous Human State Classification

Thipprachak,

Tangamchit,

Lerspalungsanti

2024

IEEE Access

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Periodic Physical Activity Information Segmentation, Counting and Recognition From Video

Cited by 6 publications

References 63 publications

PAR-Net: An Enhanced Dual-Stream CNN–ESN Architecture for Human Physical Activity Recognition

PAR-Net: An Enhanced Dual-Stream CNN–ESN Architecture for Human Physical Activity Recognition

Skeleton Based Keyframe Detection Framework for Sports Action Analysis: Badminton Smash Case

Privacy-Preserving Fall Detection Using an Ultra-Wideband Sensor With Continuous Human State Classification

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