Article Text

Download PDFPDF
Employing body-fixed sensors and machine learning to predict physical activity in military personnel
  1. Nikolaos Papadakis1,
  2. K Havenetidis2,
  3. D Papadopoulos1 and
  4. A Bissas3
  1. 1 Mathematics & Engineering Sciences, Hellenic Army Academy, Vari, Attiki, Greece
  2. 2 Physical and Cultural Education, Hellenic Army Academy, Vari, Attiki, Greece
  3. 3 School of Sport & Exercise, University of Gloucestershire, Gloucester, UK
  1. Correspondence to Professor K Havenetidis, Physical and Cultural Studies, Hellenic Army Academy, Vari 16673, Attiki, Greece; khavenetidis{at}


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.

  • sports medicine
  • health informatics
  • information technology
  • physiology
  • telemedicine
  • education & training (see medical education & training)

Data availability statement

Data are available on reasonable request.

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.


  • Contributors NP: designed the study, collected the data, analysed the data and edited the manuscript. KH: designed the study, collected the data, wrote and edited the manuscript. DP: designed the study, analysed the data and edited the manuscript. AB: analysed the data, wrote and edited the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.