| Application of inertial measurmnt units (IMUs) in running biomechanics and overuse injury risk assessment: A Narrative review |
| Paper ID : 1473-SPORTCONGRESS |
| Authors |
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amir hossein vaghari gargari1, Akram Eskandari *2 1دانشجوی دکترای آسیب شناسب ورزشی وحرکات اصلاحی پردیس بین المللی کیش دانشگاه تهران 2دانشجوی دکترای آسیب شناسی ورزشی و حرکات اصلاحی پردیس کاسپین دانشگاه تهران |
| Abstract |
| Background: Running is a popular and low-cost activity; however, recurring lower limb injuries—particularly in the knee and calf—remain a major challenge for runners. Wearable devices, including inertial measurement unit (IMU) sensors, have recently attracted considerable attention for their potential to predict and prevent these injuries. Objective: This review aims to examine and contrast recent research on the role and accuracy of IMU and smart sensors in forecasting running-related injuries, with an emphasis on sensor placement, data types, analytical algorithms, and their relationship with injury incidence. Methods: Seven major publications published between 2021 and 2025 were analyzed, including works by Horsley (2021), Zeng (2022), Mason (2022), Neal (2024), Li (2024), Arzehgar (2025), and Lucia (2025). The review incorporated systematic reviews, scoping studies, and field studies that evaluated sensor location, data acquisition types, motion analysis algorithms, and the association of these variables with injury occurrence in runners. Results: Although studies vary in their conclusions regarding joint motion analysis and immediate injury prediction, there is general consensus on the high accuracy of IMUs in quantifying spatiotemporal running variables. Horsley and Zeng suggested that sensor location had minimal impact, whereas Lucia demonstrated that positioning could significantly influence accuracy. Neal highlighted that training intensity and abrupt workload increases play a larger role in injury incidence than movement parameters, while Mason and Arzehgar emphasized the importance of comprehensive motion pattern analysis. Li’s study further indicated that integrating IMU data with artificial intelligence and machine learning algorithms can enhance injury prediction accuracy. Conclusion: IMUs and smart sensors provide valuable tools for studying running in real-world conditions. The integration of biomechanical data with physiological variables and machine learning algorithms holds promise for developing individualized models to predict and prevent running-related injuries effectively. |
| Keywords |
| Inertial Measurement Units (IMUs) - Running-related injuries - Injury prediction |
| Status: Abstract Accepted (Poster Presentation) |
