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

TL;DR

This paper introduces a grey-box Bayesian optimization method that efficiently determines optimal sensor placements in indoor environments for assisted living, improving activity recognition accuracy while reducing evaluation costs.

Contribution

It presents a novel approach that integrates domain-specific knowledge into Bayesian optimization for sensor placement, outperforming existing black-box methods in efficiency and accuracy.

Findings

Achieves higher F1-scores in activity recognition.

Reduces the number of expensive function queries by 51.3%.

Performs well in both simulated and real-world environments.

Abstract

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.