Wireless health monitoring using passive WiFi sensing

Khan, Usman Mahmood; Kabir, Zain; Hassan, Syed Ali

doi:10.1109/iwcmc.2017.7986552

Cited by 12 publications

(4 citation statements)

References 10 publications

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“…Compared with ZigBee and Bluetooth, WiFi has higher power consumption and has some privacy protection issues, which is not suitable for mobile computing scenarios. But indoor monitoring and activities in the identified smart health scenarios, due to the universality of indoor WiFi today, WiFi is gradually developing as the main solution [63,64].…”

Section: Standardsmentioning

confidence: 99%

MEC-IoT-Healthcare: Analysis and Prospects

Wang¹,

Dauwed²,

Khan³

et al. 2023

Computers, Materials &Amp; Continua

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Physical sensors, intelligent sensors, and output recommendations are all examples of smart health technology that can be used to monitor patients' health and change their behavior. Smart health is an Internet-of-Things (IoT)-aware network and sensing infrastructure that provides real-time, intelligent, and ubiquitous healthcare services. Because of the rapid development of cloud computing, as well as related technologies such as fog computing, smart health research is progressively moving in the right direction. Cloud, fog computing, IoT sensors, blockchain, privacy and security, and other related technologies have been the focus of smart health research in recent years. At the moment, the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance, including cloud storage, retrieval, and calculation of health big data. This article reviews state-of-the-art edge computing methods that has shifted to the collection, transmission, and calculation of health data, which includes various sensors and wearable devices used to collect health data, various wireless sensor technologies, and how to process health data and improve edge performance, among other things. Finally, the typical smart health application cases, blockchain's application in smart health, and related privacy and security issues were reviewed, as well as future difficulties and potential for smart health services. The comparative analysis provides a reference for the the mobile edge computing in healthcare systems.

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Section: Standardsmentioning

confidence: 99%

MEC-IoT-Healthcare: Analysis and Prospects

Wang¹,

Dauwed²,

Khan³

et al. 2023

Computers, Materials &Amp; Continua

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“…No contexto do monitoramento de Pontos de Acesso (APs) WiFi, diversas propostas visam a coleta e análise de sinal e outras características do meio [Khan et al 2017, Gebru et al 2022. Tais abordagens variam entre o uso de controladores para supervisionar o desempenho ou a utilizac ¸ão de APs independentes que executam comandos baseados em ICMP (Internet Control Message Protocol), como o ping, ou até mesmo scripts mais complexos.…”

Section: Monitoramento De Wifiunclassified

Monitoramento da Última Milha do eduroam com WiFiMon

J. Júnior,

Rocha,

Torres

et al. 2023

Anais Estendidos Do XXIII Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg Estendido 2023)

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“…In recent years, the utilization of WiFi channel state information (CSI) for human action recognition has garnered significant attention due to its non-intrusive nature and potential for application in various fields such as healthcare monitoring [1], security surveillance [2], and human-computer interaction [3]. Leveraging advanced machine learning techniques, particularly convolutional neural networks (CNNs), researchers have reported remarkable accuracy rates in identifying human actions solely based on WiFi CSI data.…”

Section: Introductionmentioning

confidence: 99%

Critical Analysis of Data Leakage in WiFi CSI-Based Human Action Recognition Using CNNs

Varga

2024

Sensors

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WiFi Channel State Information (CSI)-based human action recognition using convolutional neural networks (CNNs) has emerged as a promising approach for non-intrusive activity monitoring. However, the integrity and reliability of the reported performance metrics are susceptible to data leakage, wherein information from the test set inadvertently influences the training process, leading to inflated accuracy rates. In this paper, we conduct a critical analysis of a notable IEEE Sensors Journal study on WiFi CSI-based human action recognition, uncovering instances of data leakage resulting from the absence of subject-based data partitioning. Empirical investigation corroborates the lack of exclusivity of individuals across dataset partitions, underscoring the importance of rigorous data management practices. Furthermore, we demonstrate that employing data partitioning with respect to humans results in significantly lower precision rates than the reported 99.9% precision, highlighting the exaggerated nature of the original findings. Such inflated results could potentially discourage other researchers and impede progress in the field by fostering a sense of complacency.

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Wireless health monitoring using passive WiFi sensing

Cited by 12 publications

References 10 publications

MEC-IoT-Healthcare: Analysis and Prospects

MEC-IoT-Healthcare: Analysis and Prospects

Monitoramento da Última Milha do eduroam com WiFiMon

Critical Analysis of Data Leakage in WiFi CSI-Based Human Action Recognition Using CNNs

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