Sit-to-Stand Analysis in the Wild using Silhouettes for Longitudinal Health Monitoring

We present the first fully automated Sit-to-Stand or Stand-to-Sit (StS) analysis framework for long-term monitoring of patients in free-living environments using video silhouettes. Our method adopts a coarse-to-fine time localisation approach, where a deep learning classifier identifies possible StS sequences from silhouettes, and a smart peak detection stage provides fine localisation based on 3D bounding boxes. We tested our method on data from real homes of participants and monitored patients undergoing total hip or knee replacement. Our results show 94.4% overall accuracy in the coarse localisation and an error of 0.026 m/s in the speed of ascent measurement, highlighting important trends in the recuperation of patients who underwent surgery

Trajectory learning for robot programming by demonstration using hidden markov model and dynamic time warping

The main objective of this paper is to develop an efficient method for learning and reproduction of complex trajectories for robot programming by demonstration. Encoding of the demonstrated trajectories is performed with hidden Markov model, and generation of a generalized trajectory is achieved by using the concept of key points. Identification of the key points is based on significant changes in position and velocity in the demonstrated trajectories. The resulting sequences of trajectory key points are temporally aligned using the multidimensional dynamic time warping algorithm, and a generalized trajectory is obtained by smoothing spline interpolation of the clustered key points. The principal advantage of our proposed approach is utilization of the trajectory key points from all demonstrations for generation of a generalized trajectory. In addition, variability of the key points' clusters across the demonstrated set is employed for assigning weighting coefficients, resulting in a generalization procedure which accounts for the relevance of reproduction of different parts of the trajectories. The approach is verified experimentally for trajectories with two different levels of complexity. \ua9 2012 IEEE.Peer reviewed: YesNRC publication: Ye

View-invariant Pose Analysis for Human Movement Assessment from RGB Data

International audienceWe propose a CNN regression method to generate high-level, view-invariant features from RGB images which are suitable for human pose estimation and movement quality analysis. The inputs to our network are body joint heatmaps and limb-maps to help our network exploit geometric relationships between different body parts to estimate the features more accurately. A new multiview and multimodal human movement dataset is also introduced to evaluate the results of the proposed method. We present comparative experimental results on pose estimation using a manifold-based pose representation built from motion-captured data. We show that the new RGB derived features provide pose estimates of similar or better accuracy than those produced from depth data, even from single views only