A data-driven framework for assessing soldier performance, health, and survivability. (October 2022)
- Record Type:
- Journal Article
- Title:
- A data-driven framework for assessing soldier performance, health, and survivability. (October 2022)
- Main Title:
- A data-driven framework for assessing soldier performance, health, and survivability
- Authors:
- Mavor, Matthew P.
Gruevski, Kristina M.
Ross, Gwyneth B.
Akhavanfar, Mohammadhossein
Clouthier, Allison L.
Bossi, Linda L.M.
Karakolis, Thomas
Graham, Ryan B. - Abstract:
- Abstract: Presented is a framework that uses pattern classification methods to incrementally morph whole-body movement patterns to investigate how personal (sex, military experience, and body mass) and load characteristics affect the survivability tradespace: performance, musculoskeletal health, and susceptibility to enemy action. Sixteen civilians and 12 soldiers performed eight military-based movement patterns under three body-borne loads: ∼5.5 kg, ∼22 kg, and ∼38 kg. Our framework reduces dimensionality using principal component analysis and uses linear discriminant analysis to classify groups and morph movement patterns. Our framework produces morphed whole-body movement patterns that emulate previously published changes to the survivability tradespace caused by body-borne loads. Additionally, we identified that personal characteristics can greatly impact the tradespace when carrying heavy body-borne loads. Using our framework, military leaders can make decisions based on objective information for armour procurement, employment of armour, and battlefield performance, which can positively impact operational readiness and increase overall mission success. Highlights: A novel framework to assess the survivability trade space in soldiers. Morphed movements can represent intermediate movements that were not collected. Movements exportable for MSK modeling, animations, and susceptibility calculations. Personal and body-borne load characteristics affect the survivabilityAbstract: Presented is a framework that uses pattern classification methods to incrementally morph whole-body movement patterns to investigate how personal (sex, military experience, and body mass) and load characteristics affect the survivability tradespace: performance, musculoskeletal health, and susceptibility to enemy action. Sixteen civilians and 12 soldiers performed eight military-based movement patterns under three body-borne loads: ∼5.5 kg, ∼22 kg, and ∼38 kg. Our framework reduces dimensionality using principal component analysis and uses linear discriminant analysis to classify groups and morph movement patterns. Our framework produces morphed whole-body movement patterns that emulate previously published changes to the survivability tradespace caused by body-borne loads. Additionally, we identified that personal characteristics can greatly impact the tradespace when carrying heavy body-borne loads. Using our framework, military leaders can make decisions based on objective information for armour procurement, employment of armour, and battlefield performance, which can positively impact operational readiness and increase overall mission success. Highlights: A novel framework to assess the survivability trade space in soldiers. Morphed movements can represent intermediate movements that were not collected. Movements exportable for MSK modeling, animations, and susceptibility calculations. Personal and body-borne load characteristics affect the survivability tradespace. Objective decision-making tool for armour employment and battlefield performance. … (more)
- Is Part Of:
- Applied ergonomics. Volume 104(2022)
- Journal:
- Applied ergonomics
- Issue:
- Volume 104(2022)
- Issue Display:
- Volume 104, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 104
- Issue:
- 2022
- Issue Sort Value:
- 2022-0104-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-10
- Subjects:
- Military -- Pattern classification -- Principal component analysis
Human engineering -- Periodicals
620.82 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00036870 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.apergo.2022.103809 ↗
- Languages:
- English
- ISSNs:
- 0003-6870
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1572.500000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 22279.xml