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We validated an open-source workflow to measure 3D lower extremity joint kinematics over long durations using inertial measurement units (IMUs) for healthy subjects as they performed two 10-minute trials of common lower-extremity tasks.


The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate, and capable of assessing and mitigating drift.

We computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-minute trials: walking and a repeated sequence of varied lower-extremity movements. We share these data openly as well as the scripts to complete our analyses.

Link to our data in the DataShare tab above.

Link to download OpenSim 4.2 with OpenSense: https://simtk.org/frs/?group_id=91

More information on OpenSense: https://simtk-confluence.stanford.edu/display/OpenSim/OpenSense+-+Kinematics+with+IMU+Data

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