Abstract
A ubiquitous problem facing military organisations is musculoskeletal injury (MSKI) risk identification. Recently, two research groups, each with their own funding, collaborated to address this problem. Combining their respective areas of expertise in biomechanics and physiological biomarkers, the group explored this problem in the laboratory and in the field. They have developed a machine learning model in a US Marine Corps (USMC) officer cadet cohort that identifies MSKI risk from a single jump test, identified a minimum inertial measurement unit sensor array to quantity jump and squat performance and have identified sex differences in overuse, lower-limb injury risk. This machine learning model was able to correctly predict lift to place within 4 kg using a testing data set and less than 1 kg in the training set of data. Such collaborative approaches are encouraged to address complicated research problems. To assemble an effective team, consider forming groups that best complement each other's areas of expertise and prioritise securing separate funding to ensure each group can act independently. By doing this, the group has assessed the suitability and feasibility of various wearable technologies, used machine learning to gain insights into USMC physiological training adaptations, and developed an understanding of MSKI risk profiles within this cohort.