ArticleSensors (Basel, Switzerland)2025
A Hybrid Approach for Sports Activity Recognition Using Key Body Descriptors and Hybrid Deep Learning Classifier.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Bio-Inspired Enhanced Adaptive Centered Collision Optimizer for Hyperparameter Optimization of Multi-Scale Spatio-Temporal ConvNeXt in Boxing Action Recognition.Biomimetics (Basel, Switzerland) · 2026Article
- A narrative review of deep learning applications in sports performance analysis: current practices, challenges, and future directions.BMC sports science, medicine & rehabilitation · 2025Article
- Unmanned aerial vehicle based multi-person detection via deep neural network models.Frontiers in neurorobotics · 2025Article
- Intelligent biosensors for human movement rehabilitation and intention recognition.Frontiers in bioengineering and biotechnology · 2025Article
- A novel multi-modal rehabilitation monitoring over human motion intention recognition.Frontiers in bioengineering and biotechnology · 2025Article
- Deep multimodal biomechanical analysis for lower back pain rehabilitation to improve patients stability.Frontiers in bioengineering and biotechnology · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features. Feature fusion was employed to enhance the discriminative power of the extracted data and the physical parameters calculated by different feature extraction techniques. The system utilized a hybrid CNN (Convolutional Neural Network) + RNN (Recurrent Neural Network) classifier for event recognition, with Grey Wolf Optimization (GWO) for feature selection. Experimental results showed significant accuracy, achieving 98.5% on the UCF-101 dataset and 99.2% on the YouTube dataset. Compared to state-of-the-art methods, our approach achieved better performance in event recognition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.