Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition

This paper explores the use of wearable technology (Garmin Fenix 7) to monitor physiological and psychological factors contributing to attrition during basic military training. Attrition, or the voluntary departure of recruits from the military, often results from physical and psychological challenges, such as fatigue, injury, and stress, which lead to significant costs for the military. To better understand and mitigate attrition, we designed and implemented a comprehensive and continuous data-capturing methodology to monitor 63 recruits during their basic infantry training. It's optimized for military use by being minimally invasive (for both recruits and operators), preventing data leakage, and being built for scale. We analysed data collected from two test phases, focusing on seven key psychometric and physical features derived from baseline questionnaires and physiological measurements from wearable devices. The preliminary results revealed that recruits at risk of attrition tend to cluster in specific areas of the feature space in both Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). Key indicators of attrition included low motivation, low resilience, and a stress mindset. Furthermore, we developed a predictive model using physiological data, such as sleep scores and step counts from Garmin devices, achieving a macro mean absolute error (MAE) of 0.74. This model suggests the potential to reduce the burden of daily wellness questionnaires by relying on continuous, unobtrusive monitoring.

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Publication Details

Journal
Sensors
Published
2025-03-14
DOI
https://doi.org/10.3390/s25061828
Citations
1
Primary Topic
Occupational Health and Performance
Type
article
Field-Weighted Citation Impact
5.77
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article

Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition

Maarten Slembrouck, Steven Verstockt, Jelle Vanhaeverbeke, Robbe Decorte et al.
1 citations
Sensors
Occupational Health and Performance
5.77
article

Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition

Maarten Slembrouck, Steven Verstockt, Jelle Vanhaeverbeke, Robbe Decorte, Sarah VanDen Berghe
article en
1 citations

Abstract

This paper explores the use of wearable technology (Garmin Fenix 7) to monitor physiological and psychological factors contributing to attrition during basic military training. Attrition, or the voluntary departure of recruits from the military, often results from physical and psychological challenges, such as fatigue, injury, and stress, which lead to significant costs for the military. To better understand and mitigate attrition, we designed and implemented a comprehensive and continuous data-capturing methodology to monitor 63 recruits during their basic infantry training. It's optimized for military use by being minimally invasive (for both recruits and operators), preventing data leakage, and being built for scale. We analysed data collected from two test phases, focusing on seven key psychometric and physical features derived from baseline questionnaires and physiological measurements from wearable devices. The preliminary results revealed that recruits at risk of attrition tend to cluster in specific areas of the feature space in both Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). Key indicators of attrition included low motivation, low resilience, and a stress mindset. Furthermore, we developed a predictive model using physiological data, such as sleep scores and step counts from Garmin devices, achieving a macro mean absolute error (MAE) of 0.74. This model suggests the potential to reduce the burden of daily wellness questionnaires by relying on continuous, unobtrusive monitoring.

SensorsVol. 25(6)
Ghent University (BE)
Reduced inequalities
Openalex Percentile: Top 8%
Occupational Health and Performance
5.77
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Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition — Maarten Slembrouck, Steven Verstockt, et al. · Sensors (2025) | TGRS Research Map | TGRS