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-minute 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 3-6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60 to 0.87). We observed minimal drift in the RMS differences over ten minutes; the average slopes of the linear fits to these data were near zero (-0.14 to 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, obviating 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. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

Validity and sensitivity of an inertial measurement unit-driven biomechanical model of motor variability for gait

“…Sensor-to-segment registration was performed to associate the orientation of each sensor with the corresponding segment in the model; specifically, each thigh, shank, and foot sensor was registered, respectively, to each femur, tibia, and talus body segment. Sensor orientations were converted from their local coordinate systems to the OpenSim coordinate system using the following sequence of body-fixed rotations: 180° about x, then 90° about y, and finally -90° about z. IMU segment frames were identified based on the standing pose at the start of each gait trial: fixed rotational offsets were applied to recorded IMU sensor frames based on the segment frames of the biomechanical model in a neutral standing pose (i.e., joint flexion of 0°), with heading offsets applied to individual IMU sensor frames to match the average heading and align with the anterior-posterior axis of the biomechanical model [13,14]. As with the optoelectronic-based model, joint angles in each trial were calculated via inverse kinematics; for the IMU-based model, the solver minimized axis-angle differences between the IMU segment orientations and IMU sensor orientations [14].…”

“…Sensor orientations were converted from their local coordinate systems to the OpenSim coordinate system using the following sequence of body-fixed rotations: 180° about x, then 90° about y, and finally -90° about z. IMU segment frames were identified based on the standing pose at the start of each gait trial: fixed rotational offsets were applied to recorded IMU sensor frames based on the segment frames of the biomechanical model in a neutral standing pose (i.e., joint flexion of 0°), with heading offsets applied to individual IMU sensor frames to match the average heading and align with the anterior-posterior axis of the biomechanical model [13,14]. As with the optoelectronic-based model, joint angles in each trial were calculated via inverse kinematics; for the IMU-based model, the solver minimized axis-angle differences between the IMU segment orientations and IMU sensor orientations [14]. We compensated for differences between the initial pose of the optoelectronic and IMU models by offsetting optoelectronic-based joint angle timeseries by a constant to match the neutral standing pose of the IMU model.…”

“…Wearable inertial measurement units (IMUs) offer an alternative technology for estimating joint angles that can address these limitations of optoelectronic motion capture. IMUs have been used to estimate joint kinematics since 1990 [7], with recent work showing that IMUbased kinematic models of lower-limb activities achieve absolute differences (i.e., accuracies) ranging from 1 to 10° relative to optoelectronic-based models [8][9][10][11][12][13][14] and good-excellent consistencies in the sagittal plane timeseries [8,11,15,16]. Some IMU-based kinematic models are built based on machine learning approaches [12], but most others typically involve (i) estimating the IMU orientation by fusing accelerometer and gyroscope data, (ii) estimating the anatomical segment orientation by applying a sensor-to-segment calibration, and (iii) calculating joint angles as the relative orientation between reference frames fixed to adjacent body segments (see [17] for review).…”

“…Anatomical joint constraints in the underlying kinematic model can help to mitigate drift over long durations and distances. Using the OpenSense toolkit to compute inverse kinematics of a biomechanical model with IMU inputs, Al Borno et al [14] recently demonstrated root mean squared differences (RMSD) of 3-6° for IMU-based hip flexion, hip abduction, knee flexion, and ankle dorsiflexion angles relative to optoelectronic motion capture, with near-zero drift (from 0.14 to 0.17°/min) over ten minutes of walking. This supports the earlier findings of Kok et al [25] and provides a new open-source option for constructing an IMU-based kinematic model.…”

“…The OpenSense extension of OpenSim [28,29] provides the first open-source platform for IMU-based biomechanical modelling and a free alternative to cost-prohibitive and closed-source commercial models [18]. Because Al Borno et al [14] used the magnetometer to calculate driftfree kinematics and Slade et al [13] reported kinematic drifts in their magnetometer-free solution, it remains unclear whether a magnetometer-free, IMU-based biomechanical model can provide accurate joint angles during gait beyond one minute duration. Furthermore, it is unknown whether IMU-based biomechanical models are valid for evaluating stride-to-stride variability or are sufficiently sensitive to changes in gait kinematics for evaluating fall risk.…”