Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework
Author(s)McGrath, Timothy Michael; Stirling, Leia
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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.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
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
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