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• Melissa Boswell
• Hannah O'Day

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Description

Inertial Measurement Unit (IMU) sensors are electronic devices that measure and report a body's specific force, angular rate, and sometimes the magnetic field surrounding the body, using a combination of accelerometers, gyroscopes and magnetometers. Most people interact with these devices daily, via the IMU in their smartphone that allows them accurate navigating ability. Recent advances in wearable IMU technology, however, are allowing users to capture and store hours of  kinematic data. Traditionally, kinematic data was only attainable in motion capture laboratories equipped with multiple cameras that tracked a subject's motion via reflective markers worn on the body.  With wearable, wireless IMU sensors, we have a unique opportunity - the ability to capture human motion in "natural" settings (outside of the laboratory) lasting multiple hours.  One such IMU company, Xsens, uses a 3D accelerometer, 3D gyroscope, and 3D magnetometer in each of their IMU sensors to capture full 6 degree-of-freedom tracking of body segments.

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Many studies have shown that fatigue alters running kinematics.  A review of  incidence and associated potential risk factors of lower extremity running injuries in long distance runners found that long distance training increases injury risk, with knee injuries being the most common [6].  While some studies have been able to observe these changes, motion capture limits the studies to treadmill running and most of the studies have only studied fatigue in less than 30 minutes.  IMUs can be used outside of the lab to characterize running kinematics of fatigue and the resulting injuries.  Additionally, the change in kinematics while running on different terrains can be studied which could also help prevent injury. Though this has been tried by Reenalda et al. in 2016 using Xsens IMU sensors, there were limitations with data acquisition and battery life [7]. Rapid improvements in this technology will allow for effective exploration of running kinematics and fatigue. 

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1. Xsens Technologies (2013). Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors.

2. Gait 2392 and 2354 Models - OpenSim Documentation

3. Ferrari A, Cutti A G and Cappello A 2010a A new formulation of the coefficient of multiple correlation to assess the similarity of waveforms measured synchronously by different motion analysis protocols Gait Posture 31 540–2 

4. Zhang, J. T., Novak, A. C., Brouwer, B., & Li, Q. (2013). Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics. Physiological measurement, 34(8), N63.

5. Morris, M., Iansek, R., McGinley, J., Matyas, T. and Huxham, F. (2005), Three-dimensional gait biomechanics in Parkinson's disease: Evidence for a centrally mediated amplitude regulation disorder. Mov. Disord., 20: 40–50. doi:10.1002/mds.20278

6. van Gent, R.N. et al. (2007). Incidence and determinants of lower extremity running injuries in long distance runners: A systematic review. British Journal of Sports Medicine.

7. Reenalda, J., Maartens, E., Homan, L., & Buurke, J. J. (2016). Continuous three dimensional analysis of running mechanics during a marathon by means of inertial magnetic measurement units to objectify changes in running mechanics. Journal of Biomechanics, 49(14), 3362-3367.