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

• URL: http://arxiv.org/abs/2309.05784v1
• Date: Mon, 11 Sep 2023 19:31:14 GMT
• Title: Grey-box Bayesian Optimization for Sensor Placement in Assisted Living Environments
• Authors: Shadan Golestan, Omid Ardakanian, Pierre Boulanger
• Abstract summary: We propose a novel, sample-efficient approach to find a high-quality sensor placement in an arbitrary indoor space. Our key technical contribution lies in capturing domain-specific knowledge about the spatial distribution of activities.
• Score: 1.2093182949686778
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Adaptive Sensor Placement Inspired by Bee Foraging: Towards Efficient Environment Monitoring [0.0]
  We proposed a hybrid algorithm that combines Artificial Bee Colony (ABC) with Levy flight to optimize adaptive sensor placement. By enhancing exploration and exploitation, our approach significantly improves the identification of critical hotspots.
  arXiv  Detail & Related papers  (2024-11-08T22:24:06Z)
• A physics-driven sensor placement optimization methodology for temperature field reconstruction [9.976807723785006]
  We propose a novel physics-driven sensor placement optimization (PSPO) method for temperature field reconstruction. The PSPO method significantly outperforms random and uniform selection methods, improving the reconstruction accuracy by nearly an order of magnitude.
  arXiv  Detail & Related papers  (2024-09-27T03:26:38Z)
• Localized Zeroth-Order Prompt Optimization [54.964765668688806]
  We propose a novel algorithm, namely localized zeroth-order prompt optimization (ZOPO) ZOPO incorporates a Neural Tangent Kernel-based derived Gaussian process into standard zeroth-order optimization for an efficient search of well-performing local optima in prompt optimization. Remarkably, ZOPO outperforms existing baselines in terms of both the optimization performance and the query efficiency.
  arXiv  Detail & Related papers  (2024-03-05T14:18:15Z)
• A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition [63.26015736148707]
  This paper introduces a novel methodology to resolve the issue of optimal sensor placement for Human Activity Recognition. The derived skeleton data provides a unique strategy for identifying the optimal sensor location. Our findings indicate that the vision-based method for sensor placement offers comparable results to the conventional deep learning approach.
  arXiv  Detail & Related papers  (2023-07-06T10:38:14Z)
• Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces [3.729242965449096]
  The sensor placement problem is a common problem that arises when monitoring correlated phenomena. We present a novel formulation to the SP problem based on variational approximation that can be optimized using gradient descent.
  arXiv  Detail & Related papers  (2023-02-28T19:10:12Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• Adaptive Real Time Exploration and Optimization for Safety-Critical Systems [0.0]
  We propose the ARTEO algorithm, where we cast multi-armed bandits as a programming problem subject to safety constraints. We learn the environmental characteristics through changes in optimization inputs and through exploration. Compared to existing safe-learning approaches, our algorithm does not require an exclusive exploration phase and follows the optimization goals even in the explored points.
  arXiv  Detail & Related papers  (2022-11-10T11:37:22Z)
• Learning Space Partitions for Path Planning [54.475949279050596]
  PlaLaM outperforms existing path planning methods in 2D navigation tasks, especially in the presence of difficult-to-escape local optima. These gains transfer to highly multimodal real-world tasks, where we outperform strong baselines in compiler phase ordering by up to 245% and in molecular design by up to 0.4 on properties on a 0-1 scale.
  arXiv  Detail & Related papers  (2021-06-19T18:06:11Z)
• Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups [68.8204255655161]
  We present a method to calibrate the parameters of any pair of sensors involving LiDARs, monocular or stereo cameras. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups.
  arXiv  Detail & Related papers  (2021-01-12T12:02:26Z)
• Incorporating Expert Prior in Bayesian Optimisation via Space Warping [54.412024556499254]
  In big search spaces the algorithm goes through several low function value regions before reaching the optimum of the function. One approach to subside this cold start phase is to use prior knowledge that can accelerate the optimisation. In this paper, we represent the prior knowledge about the function optimum through a prior distribution. The prior distribution is then used to warp the search space in such a way that space gets expanded around the high probability region of function optimum and shrinks around low probability region of optimum.
  arXiv  Detail & Related papers  (2020-03-27T06:18:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.