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

TL;DR

This paper introduces an automated framework for analyzing sit-to-stand movements in real-world settings using video silhouettes, enabling long-term health monitoring of patients outside clinical environments.

Contribution

It presents a novel coarse-to-fine localization method combining deep learning and peak detection for Sit-to-Stand analysis in free-living environments.

Findings

94.4% accuracy in sequence localization

0.026 m/s error in ascent speed measurement

Effective monitoring of post-surgery patient recovery

Abstract

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.