  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.

Computer Vision and Pattern Recognition