# Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance

arXiv: https://arxiv.org/abs/1708.00163 · HTML: https://mlsanity.com/paper/1708.00163/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2017.

## Built on by (most cited first, 9 of 9)

- [Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications](https://mlsanity.com/paper/2204.11786/) (2022)
- [Differentially Private Video Activity Recognition](https://mlsanity.com/paper/2306.15742/) (2023)
- [Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room](https://mlsanity.com/paper/2503.13028/) (2025)
- [Fine-grained Activities of People Worldwide](https://mlsanity.com/paper/2207.05182/) (2022)
- [You Can Wash Hands Better: Accurate Daily Handwashing Assessment with a Smartwatch](https://mlsanity.com/paper/2112.06657/) (2021)
- [A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing](https://mlsanity.com/paper/2305.05062/) (2023)
- [MPT-PAR:Mix-Parameters Transformer for Panoramic Activity Recognition](https://mlsanity.com/paper/2408.00420/) (2024)
- [Enhancing Indoor Mobility with Connected Sensor Nodes: A Real-Time, Delay-Aware Cooperative Perception Approach](https://mlsanity.com/paper/2411.02624/) (2024)
- [TouchMap-OR: Multi-View 3D Mapping of Hand-Surface Contacts](https://mlsanity.com/paper/2605.17638/) (2026)

## Abstract (arXiv)

One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection. If we can intelligently track healthcare staff, patients, and visitors, we can better understand the sources of such infections. We envision a smart hospital capable of increasing operational efficiency and improving patient care with less spending. In this paper, we propose a non-intrusive vision-based system for tracking people's activity in hospitals. We evaluate our method for the problem of measuring hand hygiene compliance. Empirically, our method outperforms existing solutions such as proximity-based techniques and covert in-person observational studies. We present intuitive, qualitative results that analyze human movement patterns and conduct spatial analytics which convey our method's interpretability. This work is a step towards a computer-vision based smart hospital and demonstrates promising results for reducing hospital acquired infections.