Grey-box Bayesian Optimization for Sensor Placement in Assisted Living
  Environments
Shadan Golestan Omid Ardakanian Pierre Boulanger
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.