Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Uwah, Salome Enoshi; Iwendi, Celestine; Sharma, Vandana; Ojo, Olayinka Anthony; Okewumi, Peter Olufeyi; Nwigwe, Simon Title A name given to the resource Deep Learning-Based Health Risk Prediction in Contact Sports Using Wearable Sensor Data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICRITO66076.2025.11241161" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRITO66076.2025.11241161</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029803555?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029803555?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Athlete Health; Collapse risk Prediction; Contact Sports; LSTM-long short-term memory; Monitoring; PAMAP2; Wearable sensors Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155421-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online