3 accepted papers
We tackle the problem of tracking the human lower body as an initial step toward an automatic motion assessment system for clinical mobility evaluation, using a multimodal system that combines Inertial Measurement Unit (IMU) data, RGB images, and point cloud depth measurements. This system applies t…
We propose a novel approach for sampling-based and control-based motion planning. We combine a representation of the environment obtained via a modified version of optimal Rapidly-exploring Random Trees (RRT*), with landmark-based output-feedback controllers obtained via Control Lyapunov Functions,…
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…