# Optimizing Hospital Room Layout to Reduce the Risk of Patient Falls

arXiv: https://arxiv.org/abs/2101.03210 · HTML: https://mlsanity.com/paper/2101.03210/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 53 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Nature-Inspired Optimization Algorithms: Challenges and Open Problems](https://mlsanity.com/paper/2003.03776/) (2020)
- [Development of a Novel Computational Model for Evaluating Fall Risk in Patient Room Design](https://mlsanity.com/paper/2008.09169/) (2020)

## Built on by (most cited first, 3 of 3)

- [StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects](https://mlsanity.com/paper/2110.10189/) (2021)
- [Rearrange Indoor Scenes for Human-Robot Co-Activity](https://mlsanity.com/paper/2303.05676/) (2023)
- [ReCo: A Dataset for Residential Community Layout Planning](https://mlsanity.com/paper/2206.04678/) (2022)

## Abstract (arXiv)

Despite years of research into patient falls in hospital rooms, falls and related injuries remain a serious concern to patient safety. In this work, we formulate a gradient-free constrained optimization problem to generate and reconfigure the hospital room interior layout to minimize the risk of falls. We define a cost function built on a hospital room fall model that takes into account the supportive or hazardous effect of the patient's surrounding objects, as well as simulated patient trajectories inside the room. We define a constraint set that ensures the functionality of the generated room layouts in addition to conforming to architectural guidelines. We solve this problem efficiently using a variant of simulated annealing. We present results for two real-world hospital room types and demonstrate a significant improvement of 18% on average in patient fall risk when compared with a traditional hospital room layout and 41% when compared with randomly generated layouts.