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

Abstract

Background: 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.

Methods: 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-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.

Results: IMU-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).

Conclusions: Our 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.

Keywords: Biomechanical model; Drift; Inertial measurement unit; Kinematics; Open-source.

Fig. 1

Root mean square (RMS) differences for IMU-based lower extremity joint kinematics over 10 min. Our open-source workflow produced IMU-based kinematics comparable to optical-based kinematics during A a 10-min period of overground walking and B a 10-min sequence of common lower-extremity movements. Median RMS differences between IMU and optical-based kinematics were 3–6° for all joint angles except hip rotation (12°) over all subjects and all minutes. Flat trends across median per-minute RMS differences highlight minimal drift over 10 min. Box plot height is equal to interquartile range with outliers (black dots) defined as values exceeding 1.5 times the interquartile range. The asterisk denotes a different y-axis range. Results shown used the complementary filter [5]

Fig. 2

IMU-based lower extremity joint kinematics in the 1st minute (top) and 10th minute (bottom). Individual subjects’ IMU-based kinematics for the right side of the body during the 1st and 10th minute of overground walking (N = 10 subjects, one subject, S1, lacked any periods of straight-walking and was omitted from this plot). Mean ± two standard deviations (sd) for optical-based kinematics is shown as a grey shaded band and individual subject means for IMU-based kinematics are shown as black lines

Fig. 3

Effect of downweighting distal IMU sensors when solving inverse kinematics. Reducing the relative weighting on the shank orientations and the feet orientations when solving inverse kinematics helped reduce mean joint angle root mean square (RMS) difference in the 10th minute. To highlight how this downweighting influenced all joint kinematics, this analysis included mean joint angle RMS differences for the four subjects who did not have IMUs excluded and results computed from the complementary filter

Fig. 4

Changes in inverse kinematics (IK) orientation differences relate to changes in sensor fusion errors. Changes in IK orientation differences (mean over all joint angles per subject) from the 1st to 10th minute were strongly correlated with changes in sensor fusion error, indicating that IK orientation differences are a helpful tool for tracking error in the sensor fusion orientation when present. Individual subjects’ data are represented by black circles, and kinematics computed with both the complementary and the proprietary filter were used