# Grey-box Bayesian Optimization for Sensor Placement in Assisted Living Environments

arXiv: https://arxiv.org/abs/2309.05784 · HTML: https://mlsanity.com/paper/2309.05784/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [OpenBox: A Generalized Black-box Optimization Service](https://mlsanity.com/paper/2106.00421/) (2021)
- [Thinking inside the box: A tutorial on grey-box Bayesian optimization](https://mlsanity.com/paper/2201.00272/) (2022)
- [Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings](https://mlsanity.com/paper/2303.01774/) (2023)
- [Bayesian Optimization over Permutation Spaces](https://mlsanity.com/paper/2112.01049/) (2021)
- [Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework](https://mlsanity.com/paper/2012.07320/) (2020)
- [Bayesian Optimization-based Combinatorial Assignment](https://mlsanity.com/paper/2208.14698/) (2022)
- [Bayesian Optimisation for Active Monitoring of Air Pollution](https://mlsanity.com/paper/2202.07595/) (2022)

## Abstract (arXiv)

Optimizing the configuration and placement of sensors is crucial for reliable fall detection, indoor localization, and activity recognition in assisted living spaces. We propose a novel, sample-efficient approach to find a high-quality sensor placement in an arbitrary indoor space based on grey-box Bayesian optimization and simulation-based evaluation. Our key technical contribution lies in capturing domain-specific knowledge about the spatial distribution of activities and incorporating it into the iterative selection of query points in Bayesian optimization. Considering two simulated indoor environments and a real-world dataset containing human activities and sensor triggers, we show that our proposed method performs better compared to state-of-the-art black-box optimization techniques in identifying high-quality sensor placements, leading to accurate activity recognition in terms of F1-score, while also requiring a significantly lower (51.3% on average) number of expensive function queries.