Smart Multimodal Telehealth-IoT System for COVID-19 Patients

Emokpae, Lloyd; Emokpae, Roland N.; Lalouani, Wassila; Younis, Mohamed

doi:10.1109/mprv.2021.3068183

Cited by 18 publications

(7 citation statements)

References 15 publications

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“…It also implements DeepSORT to track people with a distance threshold of 2 meters. Several researchers have also discussed preventive measures with technology-based solutions such as [18], [19] and AI-related studies such as [11], [12], [20], [21] have tried to intervene to help the health and medical community overcome the challenges of social COVID-19 successfully distance practice. This a previous work [22] vary from GPS-based patient localization and tracking to crowd segmentation and estimation.…”

Section: Proposed Methodsmentioning

confidence: 99%

Real-time passenger social distance monitoring with video analytics using deep learning in railway station

Dahlan

Putra

Supangkat

et al. 2022

IJEECS

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<span lang="EN-US">Recently, at the end of December, the world faced a severe problem which is a pandemic that is caused by coronavirus disease. It also must be considered by the railway station's authorities that it must have the capability of reducing the covid transmission risk in the pandemic condition. Like a railway station, public transport plays a vital role in managing the COVID-19 spread because it is a center of public mass transportation that can be associated with the acquisition of infectious diseases. This paper implements social distance monitoring with a YOLOv4 object detection model for crowd monitoring using standard CCTV cameras to track visitors using the DeepSORT algorithm. This paper used CCTV surveillance with the actual implementation in Bandung Railway Station with the accuracy at 96.5 % result on people tracking with tested in real-time processing by using minicomputer Intel(R) Xeon(R) CPU E3-1231 v3 3.40GHz RAM 6 GB around at 18 FPS.</span>

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Section: Proposed Methodsmentioning

confidence: 99%

Real-time passenger social distance monitoring with video analytics using deep learning in railway station

Dahlan

Putra

Supangkat

et al. 2022

IJEECS

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“…More information can be found in our previous publications. 12–14 Furthermore, we designed our user interface (UI) software to facilitate the operation by users within the COPD demographics and provide built-in instructions to ensure reliable data measurements.…”

Section: System Designmentioning

confidence: 99%

A wearable multi-modal acoustic system for breathing analysis

Emokpae¹,

Emokpae²,

Bowry³

et al. 2022

The Journal of the Acoustical Society of America

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Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide with over 3 × 10 6 deaths in 2019. Such an alarming figure becomes frightening when combined with the number of lost lives resulting from COVID-caused respiratory failure. Because COPD exacerbations identified early can commonly be treated at home, early symptom detections may enable a major reduction of COPD patient readmission and associated healthcare costs; this is particularly important during pandemics such as COVID-19 in which healthcare facilities are overwhelmed. The standard adjuncts used to assess lung function (e.g., spirometry, plethysmography, and CT scan) are expensive, time consuming, and cannot be used in remote patient monitoring of an acute exacerbation. In this paper, a wearable multi-modal system for breathing analysis is presented, which can be used in quantifying various airflow obstructions. The wearable multi-modal electroacoustic system employs a body area sensor network with each sensor-node having a multi-modal sensing capability, such as a digital stethoscope, electrocardiogram monitor, thermometer, and goniometer. The signal-to-noise ratio (SNR) of the resulting acoustic spectrum is used as a measure of breathing intensity. The results are shown from data collected from over 35 healthy subjects and 3 COPD subjects, demonstrating a positive correlation of SNR values to the health-scale score.

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“…Covid-19 has made us learn many lessons, including preparedness and remote treatments. Fortunately, with IoT, remote consultation is efficient [ 76 , 77 , 78 ], and sensory devices play an important role in the recognition of vitals and symptoms [ 79 ]. Over the last two years, many e-health systems have been launched to provide proper care to patients across the globe [ 80 , 81 ].…”

Section: Fundamentals Of Internet Of Things-enabled Healthcare Systemsmentioning

confidence: 99%

A Survey of Authentication in Internet of Things-Enabled Healthcare Systems

Khan

Din

Majali

et al. 2022

Sensors

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The Internet of medical things (IoMT) provides an ecosystem in which to connect humans, devices, sensors, and systems and improve healthcare services through modern technologies. The IoMT has been around for quite some time, and many architectures/systems have been proposed to exploit its true potential. Healthcare through the Internet of things (IoT) is envisioned to be efficient, accessible, and secure in all possible ways. Even though the personalized health service through IoT is not limited to time or location, many associated challenges have emerged at an exponential pace. With the rapid shift toward IoT-enabled healthcare systems, there is an extensive need to examine possible threats and propose countermeasures. Authentication is one of the key processes in a system’s security, where an individual, device, or another system is validated for its identity. This survey explores authentication techniques proposed for IoT-enabled healthcare systems. The exploration of the literature is categorized with respect to the technology deployment region, as in cloud, fog, and edge. A taxonomy of attacks, comprehensive analysis, and comparison of existing authentication techniques opens up possible future directions and paves the road ahead.

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Smart Multimodal Telehealth-IoT System for COVID-19 Patients

Cited by 18 publications

References 15 publications

Real-time passenger social distance monitoring with video analytics using deep learning in railway station

Real-time passenger social distance monitoring with video analytics using deep learning in railway station

A wearable multi-modal acoustic system for breathing analysis

A Survey of Authentication in Internet of Things-Enabled Healthcare Systems

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