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 challeng...
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1828 |
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| author | Robbe Decorte Jelle Vanhaeverbeke Sarah VanDen Berghe Maarten Slembrouck Steven Verstockt |
| author_facet | Robbe Decorte Jelle Vanhaeverbeke Sarah VanDen Berghe Maarten Slembrouck Steven Verstockt |
| author_sort | Robbe Decorte |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-13bda9d8b77643e3b0bc9476bca7faf3 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-13bda9d8b77643e3b0bc9476bca7faf32025-08-20T03:43:40ZengMDPI AGSensors1424-82202025-03-01256182810.3390/s25061828Continuous Monitoring of Recruits During Military Basic Training to Mitigate AttritionRobbe Decorte0Jelle Vanhaeverbeke1Sarah VanDen Berghe2Maarten Slembrouck3Steven Verstockt4IDLab, Ghent University-Imec, Technologiepark-Zwijnaarde 122, 9052 Ghent, BelgiumIDLab, Ghent University-Imec, Technologiepark-Zwijnaarde 122, 9052 Ghent, BelgiumDepartment of Rehabilitation Sciences, Ghent University, C. Heymanslaan 10, 9000 Ghent, BelgiumIDLab, Ghent University-Imec, Technologiepark-Zwijnaarde 122, 9052 Ghent, BelgiumIDLab, Ghent University-Imec, Technologiepark-Zwijnaarde 122, 9052 Ghent, BelgiumThis 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.https://www.mdpi.com/1424-8220/25/6/1828continuous monitoringmilitary trainingattritionreadiness-to-performsmartwatchAI |
| spellingShingle | Robbe Decorte Jelle Vanhaeverbeke Sarah VanDen Berghe Maarten Slembrouck Steven Verstockt Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition Sensors continuous monitoring military training attrition readiness-to-perform smartwatch AI |
| title | Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition |
| title_full | Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition |
| title_fullStr | Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition |
| title_full_unstemmed | Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition |
| title_short | Continuous Monitoring of Recruits During Military Basic Training to Mitigate Attrition |
| title_sort | continuous monitoring of recruits during military basic training to mitigate attrition |
| topic | continuous monitoring military training attrition readiness-to-perform smartwatch AI |
| url | https://www.mdpi.com/1424-8220/25/6/1828 |
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