Improved Joint Estimation for Body-Mounted Motion Capture Sensors Using Human Kinematics Prior Knowledge

Abstract

Measurement uncertainty is affected by several factors, including sensor resolution. In circumstances where uncertainty varies across features (e.g., distance to transducer), confidence in sensor results is particularly affected, reducing their applicability in applications such as joint estimation through body sensors. In this manuscript, a model for joint estimation based on millimeter wave point cloud data and prior knowledge of human body structure is discussed. The proposed model uses structure and kinematic information about the measured entity to reduce measurement uncertainty, even when the expected uncertainty bounds change according to distance to the transducer. This is achieved by augmenting a sensor's processing stage with additional estimation constraints using prior knowledge, and using intersection of error bounds to improve estimated values. For a body-mounted motion tracking sensor performing joint estimation, our model is compared to empirical results of two 3D convolutional neural networks, one associated with a head-mounted millimeter wave sensor, and the other associated with a body-mounted millimeter wave sensor. The joint estimation model is found to outperform the neural-network based estimator in both mounting scenarios, resulting in reduced estimation error as confirmed by an external sensor that provides ground truth.