ItemConference Paper

Deep Learning-Based Health Risk Prediction in Contact Sports Using Wearable Sensor Data

Title
Deep Learning-Based Health Risk Prediction in Contact Sports Using Wearable Sensor Data
Creator
Uwah, Salome Enoshi; Iwendi, Celestine; Sharma, Vandana; Ojo, Olayinka Anthony; Okewumi, Peter Olufeyi; Nwigwe, Simon
Description
This study presents a deep learning-based approach to predicting physiological health risks in athletes engaged in contact sports using wearable sensor data. Motivated by the need to detect early warning signs of collapse or severe fatigue, this study employs a Long Short-Term Memory (LSTM) neural network to analyse multivariate time-series data. Key physiological signals, including heart rate, body temperature, and motion, were extracted from the PAMAP2 dataset to train and validate the model. The LSTM demonstrated strong predictive performance, achieving an accuracy of 98.3% in identifying potentially dangerous physiological states. In addition to its high classification accuracy, the model effectively captured temporal dependencies in the data, underscoring its suitability for health risk prediction in dynamic, high-intensity sports environments. This study highlights the potential of wearable data and LSTMbased analysis in supporting proactive athlete health management and injury prevention. 2025 IEEE.
Source
2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Athlete Health; Collapse risk Prediction; Contact Sports; LSTM-long short-term memory; Monitoring; PAMAP2; Wearable sensors
Coverage
Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Dept. of Computer Science, Bengaluru, India; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Okewumi P.O., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Nwigwe S., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155421-7;
Format
online
Language
English
Type
Conference paper

Citation

Uwah, Salome Enoshi; Iwendi, Celestine; Sharma, Vandana; Ojo, Olayinka Anthony; Okewumi, Peter Olufeyi; Nwigwe, Simon, “Deep Learning-Based Health Risk Prediction in Contact Sports Using Wearable Sensor Data,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/26097.

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