Estimating Lower Limb Kinematics using a Reduced Wearable Sensor Count

Abstract

This paper presents an algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors. Methods:The algorithm makes novel use of a constrained Kalman filter (CKF). The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, zero velocity update, flat-floor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). Results: Evaluation of the algorithm using a Vicon-based sensor-to-segment calibration on nine participants (7 men and 2 women, weight 63.0 ± 6.8 kg, height 1.70 ± 0.06 m, age 24.6 ± 3.9 years old), with no known gait or lower body biomechanical abnormalities, who walked within a 4 × 4 m 2 capture area shows that it can track motion relative to the mid-pelvis origin with mean position and orientation (no bias) root-mean-square error (RMSE) of 5.21±1.3 cm and 16.1 ± 3.2 • , respectively. The sagittal knee and hip joint angle RMSEs (no bias) were 10.0 ± 2.9 • and 9.9 ± 3.2 • , respectively, while the corresponding correlation coefficient (CC) values were 0.87±0.08 and 0.74±0.12. Conclusion: The CKF-based algorithm was able to track the 3D pose of the pelvis, thigh, and shanks using only three inertial sensors worn on the pelvis and shanks. Significance: Due to the Kalman-filter-based algorithm's low computation cost and the relative convenience of using only three wearable sensors, gait parameters can be computed in real-time and remotely for long-term gait monitoring. Furthermore, the system can be used to inform real-time gait assistive devices.

Estimating Lower Limb Kinematics using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements

“…The proposed algorithm, L7S (for Lie seven segment), estimates the orientation of the pelvis, thighs, shanks, and feet (i.e., 7 segments) with respect the world frame, W , using either two or three IMUs. It extends the model and assumptions from our prior work [9,29] (L5S-3I, CKF-3I, that aim to estimate the kinematics of five body segments, and places IMUs on the pelvis and shanks). Two variants of the algorithm are described: L7S-3I which uses three IMUs attached at the sacrum and feet (Fig.…”

“…The algorithm L7S was evaluated on two data sets, NeuRA (NR) [9] and Total Capture Dataset (TCD) [14], as described in Table I. Raw data were captured using a commercial IMC (i.e., Xsens Awinda) compared against a benchmark OMC (i.e., Vicon).…”

“…Unless stated, calibration and system parameters similar to [9,29] were assumed. The algorithm and calculations were implemented using Matlab 2020a.…”

“…Such configurations can improve user comfort while also reducing setup time and system cost. However, utilizing fewer sensors inherently reduces the amount of kinematic information available; this information must be inferred by enforcing mechanical joint constraints [9], making dynamic balance assumptions, or using additional sensors (e.g., cameras or distance measurement [10,11]). Amongst additional sensor approaches, video-inertial systems are the most common, where IMU measurements help resolve orientation ambiguity for OMC systems [12][13][14][15].…”

“…Recent literature has shown that sparse motion capture is also possible in 3D (e.g., window-based optimization on full body segments linked by 24 ball and socket joints [24]). In our recent work, we tracked five body segments (i.e., the pelvis, thigh, and shanks) using IMUs at the pelvis and ankles using a constrained Kalman filter (CKF) where orientation was represented using quaternions [9]. Building on prior work on state estimation using a Lie group representation ( [25][26][27] for propagating uncertainty, [28] for IMC systems under OSPS configuration), we further extended their work by representing and tracking pose using Lie groups, specifically the special Euclidean group, SE(3) [29].…”