Employing body-fixed sensors and machine learning to predict physical activity in military personnel

Papadakis, Nikolaos, Havenetidis, Konstantinos, Papadopoulos, D and Bissas, Athanassios ORCID: 0000-0002-7858-9623 (2023) Employing body-fixed sensors and machine learning to predict physical activity in military personnel. BMJ Military Health, 169 (2). pp. 152-156. doi:10.1136/bmjmilitary-2020-001585

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Abstract

Introduction This was a feasibility pilot study aiming to develop and validate an activity recognition system based on a custom-made body-fixed sensor and driven by an algorithm for recognising basic kinetic movements in military personnel. The findings of this study are deemed essential in informing our development process and contributing to our ultimate aim which is to develop a low-cost and easy-to-use body-fixed sensor for military applications. Methods Fifty military participants performed a series of trials involving walking, running and jumping under laboratory conditions in order to determine the optimal, among five machine learning (ML), classifiers. Thereafter, the accuracy of the classifier was tested towards the prediction of these movements (15 183 measurements) and in relation to participants’ gender and fitness level. Results Random forest classifier showed the highest training and validation accuracy (98.5% and 92.9%, respectively) and classified participants with differences in type of activity, gender and fitness level with an accuracy level of 83.6%, 70.0% and 62.2%, respectively. Conclusions The study showed that accurate prediction of various dynamic activities can be achieved with high sensitivity using a low-cost easy-to-use sensor and a specific ML model. While this technique is in a development stage, our findings demonstrate that our body-fixed sensor prototype alongside a fully trained validated algorithm can strategically support military operations and offer valuable information to commanders controlling operations remotely. Further stages of our developments include the validation of our refined technique on a larger range of military activities and groups by combining activity data with physiological variables to predict phenomena relating to the onset of fatigue and performance decline.

Item Type:	Article
Article Type:	Article
Subjects:	G Geography. Anthropology. Recreation > GV Recreation Leisure > GV557 Sports Q Science > QA Mathematics > QA75 Electronic computers. Computer science U Military Science > U Military Science (General)
Divisions:	Schools and Research Institutes > School of Education and Science
Research Priority Areas:	Health, Life Sciences, Sport and Wellbeing
Depositing User:	Rhiannon Goodland
Date Deposited:	06 Nov 2020 10:40
Last Modified:	31 Aug 2023 09:06
URI:	https://eprints.glos.ac.uk/id/eprint/8953

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