Table 2.

Literature survey performed for similar articles related.

Authors Publisher & year	Objectives	Design methods	Limitations and strengths	Outlook measures
Seshadri Dhruv R. et al., Frontiers in digital Health, 2020 [1],	1. Commercial devices used to measure physiological metrics to monitor health status. 2. Digital Health platforms to manage the COVID-19 pandemic	Mitigating clinical trials in false positive diagnosis Commercial devices used in decision making.	Clinical pathway and role of wearable sensor technology available as commercial device for monitoring covid-19	Issue of Data privacy, Data sharing Design and develop an algorithm to accurately monitor
Nooruddin et al., Elsevier, 2019 [2].	1. IoT based invariant fall detection system in real time. 2. Alert system to rescue individuals and provide medical assistance	Raspberry pi, Arduino, Node MCU, smartphones, Accelerometer, GPS, Buzzer, GSM. Custom Embedded system to use as client devices	Workflow model used in development and deployment stages as data collection, preprocessing, model creation, training and testing and deploying model in the server	Performance evaluation as precision, sensitivity, FI score and accuracy. In future threshold-based algorithm in case of network connectivity failure
Nora El-Rashidy et al., MDPI- Electronics, 2020 [3].	1. Bridging the gap between the current context of technologies and health systems 2. Patient x-ray scan information using CNN based deep learning model to predict state of art	Framework with three layers as patient, cloud, and hospital. Deep learning classification model architecture with Dataset to pretrain and predict, evaluate new classifier	Power consumption of wireless sensors, vital signs aggregate automatic transmission	Early detection and isolation of infected patient, effectiveness in cloud monitoring, x-ray dataset and transfer learning
Leonardo Acho et al., actuators, MDPI, 2020 [4].	1. Patient pulmonary condition for monitor and detect healthy or unhealthy situation. 2. Illustration of Potential benefits using mechanical ventilator	Intensive care units (ICU) use respiratory frequency, patient air volume and respiration cycle to define inhale and exhale. Pressure sensor data, Arduino, Raspberry pi, interface, servomotor, monitor, Resuscitator bag	1. Fault is in equipment operation to classify. 2.Misinterpretation of signals collected from healthy and unhealthy patients	Numerical method to classify and clegg- integrator philosophy for lung monitoring system
Aras R. Dagazany et al., Hindawi, 2019 [5].	1. Analyze and collect wearable big data. 2. Decision making process with recognition of spatial patterns	Deep learning for CNS and brain, spinal cord for IoT and data transfer, peripheral nervous system to sense the skin to cloud servers	Unlabeled big data, complexity, data reliability and computational bottlenecks	Massive data, heterogenous, frequency, supporting elderly population, Decision making
Qureshi, Fayez et al. Sensors. (2020) [6].	1. Use of Internet of medical things to design wearable devices. 2. Front end API for Biomedical wearable	Wearable PPG, Accelerometer, EMG sensors, Edge computing using Raspberry pi, Dashboard as physician	Sample of Biomedical signals, design factors as economic cost, human precautions	Arduino, Microphone and cloud space, sensors cost, comparative studies
AKM Jahangir Alam Majumder, Hindawi, 2019 [7].	1. Body Area sensor IoT system to collect data in providing early warning of cardiac Arrest. 2.Low power communication module to collect temperature and heart rates on smart phone	System architecture with Arduino Uno, Bluetooth chip, pulse sensor, temperature sensor. The model data collection, data transmission, Data analysis and Emergency contact information. Prediction window algorithm with 50% threshold	Power consumption rate for whole working cycle. Durability and long-term feasibility	Healthy and unhealthy test performance with ECG signal analysis. Galvanic skin response and accelerometer
Petrovic et al. (2020). IcETRAN [8]	IoT based solution to provide indoor safety through social distancing, mask detection and temperature sensing contact less	Arduino uno, thermal camera, Raspberry pi, Open CV, MQTT, Mask detection algorithm, mobile Application	Limitations in performance due to number of processed frames per second but opensource software reliable	Accuracy and frame rate for mask detection, social distance, and temperature sense
Anto Arockia Rosaline R. (2020). Emerald insight [9]	Purpose Geo fencing and tracking of Covid zones to monitor the people and alerting on mobile	Virtual perimeter monitoring system with wireless infrastructure. Bluetooth, wi-fi, GPS, Mobile application	Bluetooth option to ensure data security but short distance and privacy are the concerns	Proximity range accuracy at different range levels. Central monitoring system through Application
Hiba Asri et al. (2019). Journal of big data, springer open [10]	To predict patterns with wearable health sensors and interact with mobile phones	Heart rate sensor, Temperature sensor, Activity sensor, IoT systems, Arduino, Raspberry pi, K means clustering algorithm. Clustering by means of Elbow and silhouette method, Apache spark data bricks	Data collection from sensors Arduino uno and Raspberry pi, Android studio for coding and big data to analyze data mining, server for interaction with mobile phone	Processing time, effectiveness and K-means algorithm for clustering data, Reliability of predictive results