Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework
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sensors-20-06887-v2.pdf
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db463effe319691627c93da176c38ddb
Author(s) •
McGrath, Timothy Michael
Stirling, Leia
Date Issued
December 2020
Journal
Sensors
Publisher
MDPI AG
Citation
McGrath, Timothy and Leia Stirling. "Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework." Sensors 20, 23 (December 2020): 6887 © 2020 The Authors
Version
Final published version
Abstract
Traditionally, inertial measurement units- (IMU) based human joint angle estimation requires a priori knowledge about sensor alignment or specific calibration motions. Furthermore, magnetometer measurements can become unreliable indoors. Without magnetometers, however, IMUs lack a heading reference, which leads to unobservability issues. This paper proposes a magnetometer-free estimation method, which provides desirable observability qualities under joint kinematics that sufficiently excite the lower body degrees of freedom. The proposed lower body model expands on the current self-calibrating human-IMU estimation literature and demonstrates a novel knee hinge model, the inclusion of segment length anthropometry, segment cross-leg length discrepancy, and the relationship between the knee axis and femur/tibia segment. The maximum a posteriori problem is formulated as a factor graph and inference is performed via post-hoc, on-manifold global optimization. The method is evaluated (N = 12) for a prescribed human motion profile task. Accuracy of derived knee flexion/extension angle (4.34∘ root mean square error (RMSE)) without magnetometers is similar to current state-of-the-art with magnetometer use. The developed framework can be expanded for modeling additional joints and constraints.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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DOI of Published Version
https://doi.org/10.3390/s20236887