Assessing the Soldier Survivability Tradespace Using a Single IMU
Soldier burden is influenced by the environment, metabolic demands, equipment properties, and psychological stressors; however, much of our knowledge of soldier burden is in the context of body-borne load mass in controlled laboratory environments. Thus, to further our understanding of how all aspec...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10154132/ |
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| author | Matthew P. Mavor Victor C. H. Chan Kristina M. Gruevski Linda L. M. Bossi Thomas Karakolis Ryan B. Graham |
| author_facet | Matthew P. Mavor Victor C. H. Chan Kristina M. Gruevski Linda L. M. Bossi Thomas Karakolis Ryan B. Graham |
| author_sort | Matthew P. Mavor |
| collection | DOAJ |
| description | Soldier burden is influenced by the environment, metabolic demands, equipment properties, and psychological stressors; however, much of our knowledge of soldier burden is in the context of body-borne load mass in controlled laboratory environments. Thus, to further our understanding of how all aspects of soldier burden affect the survivability tradespace (i.e., performance, health, and susceptibility to enemy action), field-based motion capture methods are needed. We developed a human activity recognition method using the deep convolutional long short-term memory neural network architecture, trained using a single inertial measurement unit on the upper back, to identify eleven tactical movement patterns commonly performed by soldiers. Using a two-step logical algorithm, real-world constraints are forced, and class labels are expanded to 19 movements. Presented are three models based on Indoor, Section Attack (outdoors), and a General approach. Across all three approaches, we obtained an average accuracy of 90.0%. Further, we used these predictions to calculate meaningful tradespace metrics, which had an excellent agreement with calculations using the true labels. Military leaders and defence scientists can use this approach to quantify tradespace metrics in the field, as a preprocessing tool to supplement other technology, and make data-driven decisions that can help improve performance, decrease susceptibility, and increase overall mission success. |
| format | Article |
| id | doaj-art-3b3fe6af719c457bbf5e3596b68d6043 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3b3fe6af719c457bbf5e3596b68d60432024-12-11T00:01:25ZengIEEEIEEE Access2169-35362023-01-0111697626977210.1109/ACCESS.2023.328630510154132Assessing the Soldier Survivability Tradespace Using a Single IMUMatthew P. Mavor0https://orcid.org/0000-0002-9350-1650Victor C. H. Chan1Kristina M. Gruevski2Linda L. M. Bossi3Thomas Karakolis4Ryan B. Graham5https://orcid.org/0000-0001-7502-8065Faculty of Health Sciences, School of Human Kinetics, University of Ottawa, Ottawa, CanadaFaculty of Health Sciences, School of Human Kinetics, University of Ottawa, Ottawa, CanadaDefence Research and Development Canada, Toronto Research Centre, Government of Canada, Toronto, CanadaDefence Research and Development Canada, Toronto Research Centre, Government of Canada, Toronto, CanadaDefence Research and Development Canada, Toronto Research Centre, Government of Canada, Toronto, CanadaFaculty of Health Sciences, School of Human Kinetics, University of Ottawa, Ottawa, CanadaSoldier burden is influenced by the environment, metabolic demands, equipment properties, and psychological stressors; however, much of our knowledge of soldier burden is in the context of body-borne load mass in controlled laboratory environments. Thus, to further our understanding of how all aspects of soldier burden affect the survivability tradespace (i.e., performance, health, and susceptibility to enemy action), field-based motion capture methods are needed. We developed a human activity recognition method using the deep convolutional long short-term memory neural network architecture, trained using a single inertial measurement unit on the upper back, to identify eleven tactical movement patterns commonly performed by soldiers. Using a two-step logical algorithm, real-world constraints are forced, and class labels are expanded to 19 movements. Presented are three models based on Indoor, Section Attack (outdoors), and a General approach. Across all three approaches, we obtained an average accuracy of 90.0%. Further, we used these predictions to calculate meaningful tradespace metrics, which had an excellent agreement with calculations using the true labels. Military leaders and defence scientists can use this approach to quantify tradespace metrics in the field, as a preprocessing tool to supplement other technology, and make data-driven decisions that can help improve performance, decrease susceptibility, and increase overall mission success.https://ieeexplore.ieee.org/document/10154132/Activity recognitionperformanceLSTMmilitarywearablesDNN |
| spellingShingle | Matthew P. Mavor Victor C. H. Chan Kristina M. Gruevski Linda L. M. Bossi Thomas Karakolis Ryan B. Graham Assessing the Soldier Survivability Tradespace Using a Single IMU IEEE Access Activity recognition performance LSTM military wearables DNN |
| title | Assessing the Soldier Survivability Tradespace Using a Single IMU |
| title_full | Assessing the Soldier Survivability Tradespace Using a Single IMU |
| title_fullStr | Assessing the Soldier Survivability Tradespace Using a Single IMU |
| title_full_unstemmed | Assessing the Soldier Survivability Tradespace Using a Single IMU |
| title_short | Assessing the Soldier Survivability Tradespace Using a Single IMU |
| title_sort | assessing the soldier survivability tradespace using a single imu |
| topic | Activity recognition performance LSTM military wearables DNN |
| url | https://ieeexplore.ieee.org/document/10154132/ |
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