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.

翻译：在辅助生活空间中，传感器配置与布局的优化对于实现可靠的跌倒检测、室内定位及活动识别至关重要。我们提出了一种新颖且高效样本的新方法，通过基于灰箱贝叶斯优化与仿真评估，在任意室内空间找到高质量的传感器布局。我们的核心技术贡献在于捕获关于活动空间分布的领域知识，并将其融入贝叶斯优化中查询点的迭代选择过程。通过在两个模拟室内环境及一个包含人类活动与传感器触发事件的实际数据集上的实验表明，与最先进的黑箱优化技术相比，所提方法在识别高质量传感器布局方面表现更优，能够以F1分数衡量的高精度实现活动识别，同时显著降低（平均降低51.3%）昂贵函数查询的次数。