OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations

Journal of NeuroEngineering and Rehabilitation

James Dunne¹  Jennifer Hicks¹  Ayman Habib¹  Johanna O’Day¹  Carmichael Ong¹  Scott Uhlrich²  Scott Delp³  Ajay Seth⁴  Mazen Al Borno⁵  Vanessa Ibarra⁶ 

[1] Department of Bioengineering, Stanford University, Stanford, CA, USA;Department of Bioengineering, Stanford University, Stanford, CA, USA;Department of Mechanical Engineering, Stanford University, Stanford, CA, USA;Department of Bioengineering, Stanford University, Stanford, CA, USA;Department of Mechanical Engineering, Stanford University, Stanford, CA, USA;Department of Orthopaedic Surgery, Stanford University, Stanford, CA, USA;Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands;Department of Computer Science and Engineering, University of Colorado, Denver, CO, USA;Center for Bioengineering, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA;Department of Mechanical Engineering, Stanford University, Stanford, CA, USA;

关键词: Inertial measurement unit;    Open-source;    Kinematics;    Biomechanical model;    Drift;   

DOI  :  10.1186/s12984-022-01001-x

来源: Springer

【 摘 要 】

BackgroundThe 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.MethodsWe 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-min trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject’s RMS differences over time.ResultsIMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3 and 6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60–0.87). We observed minimal drift in the RMS differences over 10 min; the average slopes of the linear fits to these data were near zero (− 0.14–0.17 deg/min).ConclusionsOur workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, which may obviate the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches when studying similar activities. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

【 授权许可】

CC BY