Novel Approaches for Investigating the Soldier Survivability Tradespace - Mavor, Matthew (Université d'Ottawa / University of Ottawa, 2022)
The overarching goal of this work was to develop novel data collection and analysismethods to better understand how soldier burden affects the soldier survivability tradespace (i.e.,performance, musculoskeletal health, and susceptibility to enemy action). To achieve this goal,three studies were completed: 1) a mobile inertial measurement unit (IMU) suit was validatedagainst an optical motion capture (OPT) system; 2) data from the IMU suit was used to develop aframework for morphing movement patterns to represent intermediary body-borne load massesand personal characteristics; and 3) a single IMU was used to develop a human activity recognitionalgorithm and calculate tradespace metrics.In study one, a whole-body IMU suit (MVN Link, Xsens, Netherlands) was validatedagainst an OPT system (Vantage V5, Vicon, United Kingdom) for military-based movementsusing the root mean squared error (RMSE) of joint angles and Pearson correlation coefficients ofprincipal component (PC) scores. During a standard implementation (i.e., using differentbiomechanical models and not attempting to align them; VOPT vs. XIMU), average RMSE valuesacross all tasks were less than 9° for the lower limbs but up to 40.5° for the upper limbs. Whenusing the same biomechanical model and applying an alignment procedure (VOPT vs. VIMU-CAL),RMSE values decreased to an average of 2.5º and 17.5º for the lower and upper limbs, respectively.Of the 48 retained PCs, 38 (79%) had scores with a high or very high positive correlation (> +0.70)between the OPT and IMU systems, 15 (31%) of which had scores with a very high correlation (>+0.90). The average Pearson correlation coefficient was 0.81 (SD = 0.14). Given these results, theIMU system was deemed appropriate for collecting military-based movement patterns.In study two, principal component analysis (PCA) and linear discriminant analysis (LDA)were used to generate whole-body morphable movement patterns to represent intermediary body-ixborne loads and personal characteristics (sex, body mass, military experience). Reconstructedmovements were used for animation, musculoskeletal modelling, exposure time calculations, andsusceptibility calculations; all calculated values were comparable to previous research. Thisproject displayed that a relatively small representative dataset can be used to simulate the changein whole-body movement patterns caused by many different body-borne loads and personalcharacteristics not originally collected. By implementing this framework, defence scientists canreduce the amount and complexity of data collections needed to better understand the impact onthe survivability tradespace caused by all types of soldier burden.Study three focused on developing a deployable method for calculating tradespace metricsin the field. Three deep neural network (DNN) architectures were trained to identify eleven classlabels using data from a single IMU on the upper back. Data were collected during an indoorlaboratory-based protocol and an outdoor simulated two-person section attack. The predictionsmade by the DNNs were processed through a two-step logical algorithm to apply real-worldconstraints and expand the predictions to 19 class labels. The deep convolutional long short-termneural network architecture outperformed the convolutional neural network and fully-connectedneural network for all three approaches: indoor only, section attack only, and general. Movementswere identified with a high degree of accuracy (> 87% for accuracy and weighted F1-score), andtradespace metrics were calculated within 0.17 seconds, 0.21 shots, and 1.25% susceptibilitycompared to the tradespace metrics calculated from the ground truth labels.Overall, the data-driven methods developed throughout this dissertation can be used bydefence scientists and military leaders to improve the understanding of the survivabilitytradespace, which has the potential to improve the quality of life of soldiers, making them more fitand ready to fight, thus increasing the likelihood of mission success.
Keywords: Biomechanics; Military; Machine Learning; Pattern Recognition; Wearables