Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data.
Mustafa Musa JaberThamer AlameriMohammed Hasan AliAdi Al SyoufMohammad Al-BsheishBadr Khalaf AldhmadiSarah Yahya AliSura Khalil AbdSaif Mohammed AliWaleed AlbakerMu'taman Khalil Mohmoud JarrarPublished in: Sensors (Basel, Switzerland) (2022)
Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient's health condition remotely. Every layer has specific functionality in the COVID-19 symptoms' monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.
Keyphrases
- public health
- health information
- healthcare
- sars cov
- coronavirus disease
- mental health
- case report
- risk factors
- social media
- deep learning
- big data
- health promotion
- squamous cell carcinoma
- endothelial cells
- end stage renal disease
- human health
- newly diagnosed
- blood pressure
- physical activity
- climate change
- global health
- prognostic factors
- neoadjuvant chemotherapy
- machine learning
- lymph node