Deep Learning-Based Prediction of Physical Activity Intensity for Athletes
- Title
- Deep Learning-Based Prediction of Physical Activity Intensity for Athletes
- Creator
- Indhumathi G.; Sandhiya S.; Kantha D.; Hemalatha P.K.; Rakesh K.R.; Chandrasekar T.
- Description
- Maximizing training plans for athletes and lowering the risk of injury depends on a precise assessment of the degree of physical activity. Existing system in-use systems often employ simplistic models, which leads to inaccurate projections. The paper presents a deep learning-based system that uses convolutional neural networks (CNNs) to create real-time predictions using wearable sensor data. Because it automatically extracts relevant features from raw sensor data, the technique does not need human feature engineering. Utilizing thorough model training and evaluation, it exceeded the most recent methods in terms of accuracy (0.92), precision (0.90), recall (0.92), F1-score (0.91), and ROC AUC (0.94). Results of cross-validation over many data subsets confirm the resilience of the method. Comparisons of confusion matrices also demonstrate how effectively the algorithm forecasts various activity intensities. Overall, the proposed system represents a breakthrough in accurately estimating how much physical activity athletes do, enhancing the efficacy of their training, and reducing the possibility of damage in sporting settings. 2024 IEEE.
- Source
- International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- athletes; physical activity intensity prediction; real-time feedback; sensor data analysis; training regimes
- Coverage
- Indhumathi G., Rajalakshmi Engineering College, Department of Ece, Chennai, India; Sandhiya S., University College of Engineering, Department of Information Technology, Villupuram, India; Kantha D., George Washington University, Computer Science Department, WA, United States; Hemalatha P.K., Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Department of Mathematics, Chennai, India; Rakesh K.R., Christ University (Deemed to be University), Department of Psychology, Ghaziabad, India; Chandrasekar T., Velammal College of Engineering & Technology, Department of Eee, Madurai, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835036066-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Indhumathi G.; Sandhiya S.; Kantha D.; Hemalatha P.K.; Rakesh K.R.; Chandrasekar T., “Deep Learning-Based Prediction of Physical Activity Intensity for Athletes,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 18, 2025, https://archives.christuniversity.in/items/show/19082.