Publications tagged with "Health monitoring"

1. A cyber warfare perspective on risks related to health IoT devices and contact tracing

   Bobbio, A., Campanile, L., Gribaudo, M., Iacono, M., Marulli, F., & Mastroianni, M. (2023). A cyber warfare perspective on risks related to health IoT devices and contact tracing [Article]. Neural Computing and Applications, 35(19), 13823–13837. https://doi.org/10.1007/s00521-021-06720-1

   Abstract

   The wide use of IT resources to assess and manage the recent COVID-19 pandemic allows to increase the effectiveness of the countermeasures and the pervasiveness of monitoring and prevention. Unfortunately, the literature reports that IoT devices, a widely adopted technology for these applications, are characterized by security vulnerabilities that are difficult to manage at the state level. Comparable problems exist for related technologies that leverage smartphones, such as contact tracing applications, and non-medical health monitoring devices. In analogous situations, these vulnerabilities may be exploited in the cyber domain to overload the crisis management systems with false alarms and to interfere with the interests of target countries, with consequences on their economy and their political equilibria. In this paper we analyze the potential threat to an example subsystem to show how these influences may impact it and evaluate a possible consequence. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

   DOI Publisher Details Copy BibTeX Download .bib

   {"key"=>"Bobbio202313823", "type"=>"Article", "bibtex"=>"@article{Bobbio202313823,\n  author = {Bobbio, Andrea and Campanile, Lelio and Gribaudo, Marco and Iacono, Mauro and Marulli, Fiammetta and Mastroianni, Michele},\n  title = {A cyber warfare perspective on risks related to health IoT devices and contact tracing},\n  year = {2023},\n  journal = {Neural Computing and Applications},\n  volume = {35},\n  number = {19},\n  pages = {13823 – 13837},\n  doi = {10.1007/s00521-021-06720-1}\n}\n", "author"=>"Bobbio, Andrea and Campanile, Lelio and Gribaudo, Marco and Iacono, Mauro and Marulli, Fiammetta and Mastroianni, Michele", "author_array"=>[{"first"=>"Andrea", "last"=>"Bobbio", "prefix"=>"", "suffix"=>""}, {"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Marco", "last"=>"Gribaudo", "prefix"=>"", "suffix"=>""}, {"first"=>"Mauro", "last"=>"Iacono", "prefix"=>"", "suffix"=>""}, {"first"=>"Fiammetta", "last"=>"Marulli", "prefix"=>"", "suffix"=>""}, {"first"=>"Michele", "last"=>"Mastroianni", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Andrea", "author_0_last"=>"Bobbio", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Lelio", "author_1_last"=>"Campanile", "author_1_prefix"=>"", "author_1_suffix"=>"", "author_2_first"=>"Marco", "author_2_last"=>"Gribaudo", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Mauro", "author_3_last"=>"Iacono", "author_3_prefix"=>"", "author_3_suffix"=>"", "author_4_first"=>"Fiammetta", "author_4_last"=>"Marulli", "author_4_prefix"=>"", "author_4_suffix"=>"", "author_5_first"=>"Michele", "author_5_last"=>"Mastroianni", "author_5_prefix"=>"", "author_5_suffix"=>"", "title"=>"A cyber warfare perspective on risks related to health IoT devices and contact tracing", "year"=>"2023", "journal"=>"Neural Computing and Applications", "volume"=>"35", "number"=>"19", "pages"=>"13823 – 13837", "doi"=>"10.1007/s00521-021-06720-1", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123074341&doi=10.1007%2fs00521-021-06720-1&partnerID=40&md5=11f2286bd8ccb5a72af96b02accea11a", "abstract"=>"The wide use of IT resources to assess and manage the recent COVID-19 pandemic allows to increase the effectiveness of the countermeasures and the pervasiveness of monitoring and prevention. Unfortunately, the literature reports that IoT devices, a widely adopted technology for these applications, are characterized by security vulnerabilities that are difficult to manage at the state level. Comparable problems exist for related technologies that leverage smartphones, such as contact tracing applications, and non-medical health monitoring devices. In analogous situations, these vulnerabilities may be exploited in the cyber domain to overload the crisis management systems with false alarms and to interfere with the interests of target countries, with consequences on their economy and their political equilibria. In this paper we analyze the potential threat to an example subsystem to show how these influences may impact it and evaluate a possible consequence. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.", "author_keywords"=>"COVID-19; Cyber warfare; IoT; Risk analysis; Security", "keywords"=>"Cybersecurity; Health risks; Internet of things; Medical problems; Risk analysis; Risk assessment; Adopted technology; Contact tracing; Cyber warfare; Health monitoring devices; IoT; IT resources; Medical health; Security; Security vulnerabilities; Smart phones; COVID-19", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 11; All Open Access, Bronze Open Access, Green Open Access"}
       

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2. Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications

   Campanile, L., Marulli, F., Mastroianni, M., Palmiero, G., & Sanghez, C. (2021). Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 343–353. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137959400&partnerID=40&md5=eb78330cb4d585e500b77cd906edfbc7

   Abstract

   In this paper, we introduce a solution aiming to improve the accuracy of the surface temperature detection in an outdoor environment. The temperature sensing subsystem relies on Mobotix thermal camera without the black body, the automatic compensation subsystem relies on Raspberry Pi with Node-RED and TensorFlow 2.x. The final results showed that it is possible to automatically calibrate the camera using machine learning and that it is possible to use thermal imaging cameras even in critical conditions such as outdoors. Future development is to improve performance using computer vision techniques to rule out irrelevant measurements. © 2021 by SCITEPRESS - Science and Technology Publications, Lda.

   Publisher Details Copy BibTeX Download .bib

   {"key"=>"Campanile2021343", "type"=>"Conference paper", "bibtex"=>"@conference{Campanile2021343,\n  author = {Campanile, Lelio and Marulli, Fiammetta and Mastroianni, Michele and Palmiero, Gianfranco and Sanghez, Carlo},\n  title = {Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications},\n  year = {2021},\n  journal = {International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings},\n  volume = {2021-April},\n  pages = {343 – 353}\n}\n", "author"=>"Campanile, Lelio and Marulli, Fiammetta and Mastroianni, Michele and Palmiero, Gianfranco and Sanghez, Carlo", "author_array"=>[{"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Fiammetta", "last"=>"Marulli", "prefix"=>"", "suffix"=>""}, {"first"=>"Michele", "last"=>"Mastroianni", "prefix"=>"", "suffix"=>""}, {"first"=>"Gianfranco", "last"=>"Palmiero", "prefix"=>"", "suffix"=>""}, {"first"=>"Carlo", "last"=>"Sanghez", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Lelio", "author_0_last"=>"Campanile", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Fiammetta", "author_1_last"=>"Marulli", "author_1_prefix"=>"", "author_1_suffix"=>"", "author_2_first"=>"Michele", "author_2_last"=>"Mastroianni", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Gianfranco", "author_3_last"=>"Palmiero", "author_3_prefix"=>"", "author_3_suffix"=>"", "author_4_first"=>"Carlo", "author_4_last"=>"Sanghez", "author_4_prefix"=>"", "author_4_suffix"=>"", "title"=>"Machine Learning-aided Automatic Calibration of Smart Thermal Cameras for Health Monitoring Applications", "year"=>"2021", "journal"=>"International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings", "volume"=>"2021-April", "pages"=>"343 – 353", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137959400&partnerID=40&md5=eb78330cb4d585e500b77cd906edfbc7", "abstract"=>"In this paper, we introduce a solution aiming to improve the accuracy of the surface temperature detection in an outdoor environment. The temperature sensing subsystem relies on Mobotix thermal camera without the black body, the automatic compensation subsystem relies on Raspberry Pi with Node-RED and TensorFlow 2.x. The final results showed that it is possible to automatically calibrate the camera using machine learning and that it is possible to use thermal imaging cameras even in critical conditions such as outdoors. Future development is to improve performance using computer vision techniques to rule out irrelevant measurements. © 2021 by SCITEPRESS - Science and Technology Publications, Lda.", "author_keywords"=>"Clinical Evaluation; Covid-19 Disease; Deep Learning; Health Monitoring; Internet of Things; Machine Learning; Mass Screening Infection; Smart Sensor Networks", "keywords"=>"Cameras; Deep learning; Diagnosis; Diseases; Infrared devices; Infrared imaging; Learning systems; Sensor networks; Temperature sensors; Automatic calibration; Clinical evaluation; Covid-19 disease; Deep learning; Health monitoring; Machine-learning; Mass screening infection; Mass screenings; Smart sensor networks; Thermal camera; Internet of things", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 0"}
       

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