Influenza-like symptom recognition using mobile sensing and graph neural networks

Dong, Guimin; Cai, Lihua; Datta, Debajyoti; Kumar, Shashwat; Barnes, Laura E.; Boukhechba, Mehdi

doi:10.1145/3450439.3451880

Cited by 17 publications

(22 citation statements)

References 43 publications

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“…Typically, WiFi devices have been adapted for at-home healthcare targets such as respiration sensing [311] and sleep sensing [304], by analyzing their channel state information (CSI). In addition, mobile sensing paradigm, which collects data from mobile devices [278], is being used to understand and assess one's social anxiety [27,29], infectious disease risk [61], and disease progression [250], by passively sensing people's physiology, mobility, and activity.…”

Section: Autonomous Thingsmentioning

confidence: 99%

“…MCD is a ubiquitous system that is formed by a diverse set of embedded sensors (e.g., accelerometer, gyroscope, GPS), that continuously and unobtrusively collects human biobehavioral footprints. Numerous mobile sensing systems have been employed for anxiety recognition [29], symptom detection [61], and fluid intake management [256]. For example, by leveraging the embedded sensors in smartphones and smartwatches, raw mobile sensing data can be translated into behavioral markers and linked to mental health states [278].…”

Section: Human-centric Sensor Networkmentioning

confidence: 99%

“…Graph modeling can transform unstructured human state information into graph structured representation that connects individual human states effectively. In general, there are two different classes of graph modeling in HSD: 1) using graphs to generate explicit representation of human [61,63,64] produced graph representations from multi-channel human behaviors, including social interaction, mobility, and physical activity, and used Bluetooth encounters as nodes in social interaction graphs, visited places as nodes in mobility trajectory graphs, and body movement as nodes in physical activity transition graphs. In the studies of monitoring brain electrical activities, electrical signals produced from segmented regions on human scalp indicate the neuron activities in the corresponding brain region [186].…”

Section: Graph Modeling Of Human Behavior Dynamicsmentioning

confidence: 99%

“…The extra complexity in GNN empowers us to effectively encode complex relationships and interdependencies among devices in IoT sensing systems, and among data instances over time from IoT devices. A wide range of applications in IoT sensing have leveraged GNN to represent their learning problems on the associated data, including works in team collaboration [277], traffic monitoring and forecasting [40,51,112,143,321], remote scene classification [157], robotic grasping [81], movement analysis and object detection [105,237], public service [318], energy management [209], autonomous driving [32,147], social sensing [36], personal health [61] etc. GNN in IoT sensing has been met with remarkable success with exceeding or comparable performance to existing benchmarks.…”

Section: Introductionmentioning

confidence: 99%

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Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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Section: Autonomous Thingsmentioning

confidence: 99%

Section: Human-centric Sensor Networkmentioning

confidence: 99%

Section: Graph Modeling Of Human Behavior Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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“…This clusters X into "high density" regions. Density based clustering has received interest from many fields because of its applicability to a vast range of real world problems [1,16,5,18,2,15].…”

Section: Introductionmentioning

confidence: 99%

Geometric reconstructions of density based clusterings

Garcia-Pulido¹,

Samardzhiev²

2022

Preprint

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DBSCAN * and HDBSCAN * are well established density based clustering algorithms. However, obtaining the clusters of very large datasets is infeasible, limiting their use in real world applications.By exploiting the geometry of Euclidean space, we prove that it is possible to systematically construct the DBSCAN * and HDBSCAN * clusters of a finite X ⊂ R n from specific subsets of X. We are able to control the size of these subsets and therefore our results make it possible to cluster very large datasets.To illustrate our theory, we cluster the Microsoft Building Footprint Database of the US, which is not possible using the standard implementations.

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