Visual-Inertial Filtering for Human Walking Quantification

We propose a novel system to track human lower-body motion as part of a larger movement assessment system for clinical evaluation. Our system combines multiple wearable Inertial Measurement Unit (IMU) sensors and a single external RGB-D camera. We use a factor graph with a Sliding Window Filter (SWF) formulation that merges 2-D joint data extracted from the RGB images via a Deep Neural Network, raw depth information, raw IMU gyroscope readings, and estimated foot contacts extracted from IMU gyroscope and accelerometer data. For the system, we use an articulated model of human body motion based on differential manifolds. We compare the results of our system against a gold-standard motion capture system and a vision-only alternative. Our proposed system qualitatively presents smoother 3D joint trajectories when compared to noisy depth data, allowing for more realistic gait estimations. At the same time, with respect to the vision-only baseline, it improves the median of the joint trajectories by around 2 cm, while considerably reducing outliers by up to 0.6 m.