Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction
Abstract This paper presents a novel framework for accurate exercise posture recognition and health indicator prediction based on improved convolutional neural networks. We propose a multi-scale feature fusion architecture incorporating spatiotemporal attention mechanisms to enhance key point detect...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01854-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849238629396774912 |
|---|---|
| author | He Chen Rongchang Fan |
| author_facet | He Chen Rongchang Fan |
| author_sort | He Chen |
| collection | DOAJ |
| description | Abstract This paper presents a novel framework for accurate exercise posture recognition and health indicator prediction based on improved convolutional neural networks. We propose a multi-scale feature fusion architecture incorporating spatiotemporal attention mechanisms to enhance key point detection precision while maintaining computational efficiency. The system achieves superior posture recognition performance with 78.6% mAP and 91.5% PCK@0.5, outperforming state-of-the-art methods while maintaining real-time inference capabilities (27.3 FPS). For health indicator prediction, we develop a CNN-LSTM model with personalized parameter adaptation that accurately forecasts multiple physiological metrics including cardiorespiratory fitness, muscular strength, and metabolic rate, achieving 86.1–92.6% prediction accuracy across diverse health dimensions. Comprehensive evaluations on both self-collected and public datasets demonstrate the system’s robustness across varying exercise types, environmental conditions, and demographic groups. The proposed approach offers significant potential for applications in personal fitness coaching, rehabilitation monitoring, and preventive healthcare by providing automated exercise form evaluation and personalized health insights. |
| format | Article |
| id | doaj-art-7e8b331c8029499ebcc3d3a2b7571fff |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7e8b331c8029499ebcc3d3a2b7571fff2025-08-20T04:01:34ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-01854-xImproved convolutional neural network for precise exercise posture recognition and intelligent health indicator predictionHe Chen0Rongchang Fan1Ministry of Sports, Jiangsu Health Vocational CollegeMinistry of Sports, Nanjing Vocational Institute of Railway TechnologyAbstract This paper presents a novel framework for accurate exercise posture recognition and health indicator prediction based on improved convolutional neural networks. We propose a multi-scale feature fusion architecture incorporating spatiotemporal attention mechanisms to enhance key point detection precision while maintaining computational efficiency. The system achieves superior posture recognition performance with 78.6% mAP and 91.5% PCK@0.5, outperforming state-of-the-art methods while maintaining real-time inference capabilities (27.3 FPS). For health indicator prediction, we develop a CNN-LSTM model with personalized parameter adaptation that accurately forecasts multiple physiological metrics including cardiorespiratory fitness, muscular strength, and metabolic rate, achieving 86.1–92.6% prediction accuracy across diverse health dimensions. Comprehensive evaluations on both self-collected and public datasets demonstrate the system’s robustness across varying exercise types, environmental conditions, and demographic groups. The proposed approach offers significant potential for applications in personal fitness coaching, rehabilitation monitoring, and preventive healthcare by providing automated exercise form evaluation and personalized health insights.https://doi.org/10.1038/s41598-025-01854-xConvolutional neural networksPosture recognitionHealth indicator predictionSpatiotemporal attentionFeature fusionExercise monitoring |
| spellingShingle | He Chen Rongchang Fan Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction Scientific Reports Convolutional neural networks Posture recognition Health indicator prediction Spatiotemporal attention Feature fusion Exercise monitoring |
| title | Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction |
| title_full | Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction |
| title_fullStr | Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction |
| title_full_unstemmed | Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction |
| title_short | Improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction |
| title_sort | improved convolutional neural network for precise exercise posture recognition and intelligent health indicator prediction |
| topic | Convolutional neural networks Posture recognition Health indicator prediction Spatiotemporal attention Feature fusion Exercise monitoring |
| url | https://doi.org/10.1038/s41598-025-01854-x |
| work_keys_str_mv | AT hechen improvedconvolutionalneuralnetworkforpreciseexerciseposturerecognitionandintelligenthealthindicatorprediction AT rongchangfan improvedconvolutionalneuralnetworkforpreciseexerciseposturerecognitionandintelligenthealthindicatorprediction |