Estimation of running injury risks using wearable sensors - Murai, A.; Shiogama, C.; Ming, D.; Takamatsu, J.; Tada, M.; Ogasawara, T. (ISBS Proceedings Archive (Michigan), 2018)
This research estimates running pattern characteristics that relate to running injury risks quantitatively and simply from a real-environment running motion. Wearable inertial measurement unit (IMU) sensors are used to provide a simple measurement of the running patterns in a real environment. We then measure an experimental running motion in detail in the laboratory using both large-scale devices and wearable sensors, and build correlational models between the conventional parameters related to running injury risks and parameters from wearable sensors. These correlational models realize a quantitative and simple estimation of running pattern characteristics related to running injury risks from a real-environment running motion. Our models estimate that fatigue, grounding style, pronation, and grounding impact have a high correlation with injury risk by the conventional methods. A feedback of these parameters and shoe selection based on these information would contribute to a reduction of running injuries.