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• Melissa Boswell
• Hannah O'Day

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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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1. Can knee joint angle be calculated accurately with two IMU's?
2. Can the IMU data be input into OpenSim to reconstruct movement kinematics?
3. Can movements kinematics be reconstructed in OpenSim with IMU measurements in real-time?

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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. 

Conclusions

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1. Knee angle can be measured accurately using two IMUs with the Xsens pipeline.
2. Knee joint angle can be input into OpenSim to reconstruct the movement.
3. Orientations can be exported prior to feedback from the Xsens system and input into OpenSim to run an Inverse Kinematics simulation and knee joint angle can be determined.
4. Obtaining knee joint angle from an IK simulation in OpenSim is less accurate, but can be improved with feedback from the OpenSim model to update orientation and position.

Though we were able to answer our first 2 research questions, we leave our last research question for the future work.:

Can movements kinematics be reconstructed in OpenSim with IMU measurements in real-time to use OpenSim as the biomechanical model for orientation and position feedback?

Progress

•  Collect IMU measurements of knee flexion/extension with Xsens MVN Studio software
•  Input Xsens joint angle estimates into OpenSim to reconstruct movement
•  Do simultaneous IMU data collection with 2D video + goniometer to validate joint angles obtained with IMUs 
•  Determine knee joint angles from Xsens IMU orientation data from thigh and shank sensors 
  •  Get orientation from thigh and shank sensors
  •  Input into OpenSim inverse kinematics script
  •  Load motion in OpenSim, and apply to gait10dof18musc.osim model
  •  Compare with Xsens joint estimation, and 2D video + goniometer data 
•  Read through Xsens literature to better understand their biomechanical model

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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.