Optimal Sensor Placement in Body Surface Networks using Gaussian Processes

• URL: http://arxiv.org/abs/2209.02912v1
• Date: Wed, 7 Sep 2022 03:40:08 GMT
• Title: Optimal Sensor Placement in Body Surface Networks using Gaussian Processes
• Authors: Emad Alenany and Changqing Cheng
• Abstract summary: The proposed methodology incorporates the use a recent experimental design method for the selection of landmarkings on biological objects. The proposed method adds to design efforts for a more clinically practical ECGI system by improving its wearability and reduce the design cost as well.
• Score: 4.111899441919164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores a new sequential selection framework for the optimal sensor placement (OSP) in Electrocardiography imaging networks (ECGI). The proposed methodology incorporates the use a recent experimental design method for the sequential selection of landmarkings on biological objects, namely, Gaussian process landmarking (GPLMK) for better exploration of the candidate sensors. The two experimental design methods work as a source of the training and the validation locations which is fitted using a spatiotemporal Gaussian process (STGP). The STGP is fitted using the training set to predict for the current validation set generated using GPLMK, and the sensor with the largest prediction absolute error is selected from the current validation set and added to the selected sensors. Next, a new validation set is generated and predicted using the current training set. The process continues until selecting a specific number of sensor locations. The study is conducted on a dataset of body surface potential mapping (BSPM) of 352 electrodes of four human subjects. A number of 30 sensor locations is selected using the proposed algorithm. The selected sensor locations achieved average $R^2 = 94.40 \%$ for estimating the whole-body QRS segment. The proposed method adds to design efforts for a more clinically practical ECGI system by improving its wearability and reduce the design cost as well.

Related papers

• Sensor Selection via GFlowNets: A Deep Generative Modeling Framework to Navigate Combinatorial Complexity [3.255315368019651]
  This work addresses the challenge of selecting $k$ sensor elements from a set of $m$ to optimize a generic Quality-of-Service metric. evaluating all $binommk$ possible sensor subsets is impractical, leading to prior solutions using convex relaxations, greedy algorithms, and supervised learning approaches.
  arXiv  Detail & Related papers  (2024-07-29T06:56:57Z)
• Quadratic Formulation of Mutual Information for Sensor Placement Optimization using Ising and Quantum Annealing Machines [0.0]
  We address a problem to determine the placement of sensors from multiple candidate positions, aiming to maximize information acquisition with the minimum number of sensors. We defined mutual information (MI) between the data from selected sensor positions and the data from the others as an objective function. As an example, we calculated optimal solutions of the objective functions for 3 candidates of sensor placements using a quantum annealing machine.
  arXiv  Detail & Related papers  (2024-07-20T04:46:02Z)
• CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition [5.215675032361843]
  Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors. While employing numerous sensors with high-frequency sampling rates usually improves the results, it often leads to data inefficiency and unnecessary expansion of the ANN. We introduce a pragmatic framework for data-efficient utilization in HAR tasks, considering the optimization of both sensor modalities and sampling rate simultaneously.
  arXiv  Detail & Related papers  (2024-01-03T22:04:40Z)
• A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks [53.022158485867536]
  We propose a dynamic sensor selection approach for deep neural networks (DNNs) It is able to derive an optimal sensor subset selection for each specific input sample instead of a fixed selection for the entire dataset. We show how we can use this dynamic selection to increase the lifetime of a wireless sensor network (WSN) by imposing constraints on how often each node is allowed to transmit.
  arXiv  Detail & Related papers  (2023-08-16T14:04:50Z)
• 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)
• 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)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• 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)
• Learning a Probabilistic Strategy for Computational Imaging Sensor Selection [16.553234762932938]
  We propose a physics-constrained, fully differentiable, autoencoder that learns a probabilistic sensor-sampling strategy for optimized sensor design. The proposed method learns a system's preferred sampling distribution that characterizes the correlations between different sensor selections as a binary, fully-connected Ising model.
  arXiv  Detail & Related papers  (2020-03-23T17:52:17Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

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.