Chronic Obstructive Pulmonary Disease Warning in the Approximate Ward Environment

Zhang, Qing; Haider, Daniyal; Wang, Weigang; Shah, Syed Aziz; Yang, Xiaodong; Abbasi, Qammer H.

doi:10.3390/app8101915

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…In prior research works [12,13], different types of machine learning algorithms used for signal classification tasks [82][83][84]. We used four machine learning algorithms to classify WCSI data.…”

Section: ) Results Of MLmentioning

confidence: 99%

Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information

et al. 2020

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In the past decades, cognitive computing and communication densely used in lots of networking areas. Current improvement in deep learning (DL) and big data analysis create great potential to analyze cognitive intelligence (CI) for many applications such as human activity monitoring and recognition through wireless communication. Cognitive intelligence and wireless communication are using to establish smart healthcare systems. Healthcare monitoring systems turn into interesting research subjects where monitoring post-operative surgical patients are the current focal point to the researcher. In this paper, we argue that deep learning along with the wireless communication technique introduces cognitive intelligence for the healthcare monitoring system. We present a deep learning based convolutional neural network (CNN) model to classify image data and a convenient and multi-functional software-defined radio (SDR) platform to detect movement of the ankle of patients who underwent ankle fracture surgery. Capturing wireless channel state information (WCSI) in the presence of the human body and classifying using CNN to observe distinct movements is the key idea of this study. A universal software radio peripheral (USRP) platform used to capture WCSI data and used for classification. AlexNet and ZFNet both are the famous architecture of CNN and used in a parallel way to classify captured WCSI-based images that converted from numeric data. The classification established on the ankles movements after surgery and classification results show that CNN provides satisfying results where test accuracy is 98.98%.

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Section: ) Results Of MLmentioning

confidence: 99%

Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information

et al. 2020

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“…Other applications of medical implants in everyday human life for healthcare monitoring include pacemakers, defibrillators, implantable glucose sensors, pressure sensors and capsule pills, functional electrical stimulators (FES), cochlear and retinal implants, hypopnea syndrome diagnosis and chronic obstructive pulmonary disease warning etc. [8]- [16]. Implantable devices are made up of many components including antenna, battery and sensors.…”

Section: Figmentioning

confidence: 99%

Implantable Antennas for Bio-Medical Applications

Malik

Sant

Ajmal

et al. 2021

IEEE J. Electromagn. RF Microw. Med. Biol.

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Antenna design considerations for implantable devices in remote telehealth systems Take-Home Messages • Implantable sensors are pivotal to telehealth systems enabling continuous monitoring of a patient's vital health signs wirelessly. • Antennas are an integral element of these systems whose design is a complex task due to harsh and highly volatile in-body environment and requirements of robust and reliable performance while offering miniature structure, patient safety and biocompatibility. • A comprehensive critical review on the antenna design for implantable medical devices highlighting requirements, challenges, antenna types and human body effects on their performance shows that slotted patch antennas operating at higher frequencies can serve the purpose optimally. • The slotted patch antenna designed in the light of recommendations made offers a small size of 7.5×5×0.25 mm 3 with a-10 dB bandwidth of 25 MHz, a near-omnidirectional pattern and a gain of 1.7 dBi. • The paper can serve as a reference for the antenna designers working in the field of implantable devices providing state-of-the-art, current advancements, requirements, challenges as well as design rules.

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“…The monitoring of respiratory symptoms for COPD (chronic obstructive pulmonary disease) used C-band wireless sensing to detect the respiratory conditions, including coughing and normal and abnormal breathing of a COPD patient by utilizing NIC and the CSI tool for the extraction of CSI with an omni-directional antenna operating at 4.8 GHz frequency. The 5 G sensing technology enhanced the health care system for detection of various diseases effectively [ 87 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Khan

Zhang

et al. 2020

Micromachines

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The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation.

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Chronic Obstructive Pulmonary Disease Warning in the Approximate Ward Environment

Cited by 17 publications

References 21 publications

Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information

Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information

Implantable Antennas for Bio-Medical Applications

A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

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