Artificial Intelligence based prediction system for General Medicine

COVID-19 is highly infectious and has been extensively spread worldwide, with approximately 651 million definite cases crosswise the globe including Pakistan. At that era of pandemic where patients are not able to approach a doctor for even the routine checkups, in such curial situation even normal disease checkups are ignored by many families due to pandemic situations, those diseases may lead to be a perilous disease are results of it. Human disorders portray scenarios that even disturb or permanently cutoff the essential functions of a body parts. Consequently, the aim is to transform raw health data potential into actionable insights to applying the promising outcomes of Body Sensor Network (BSN) and State-of-Art Artificial Intelligence (AI) techniques to get proper medicine allocation to the particular health state of patient. In this paper the different techniques of Deep Learning and Machine Learning introduced to predict the actual medicine for the specific health state of patient according to data from the BSN. Experiments have been conducted on large dataset which shepherd it into 16 states of patient's health which will allotted to AI model to predict the medicine accordingly to the health state of patient. Experimental results show the 87.46% by Random Forest, 92.74% by K-Nearest Neighbors, 74.57% by Naive Bayes, 94.41% by Extreme Gradient Boost, 84.88% by Multi-Layer Perceptron in terms of precision of model training in event of classification. © 2023 IEEE.

Transmission Power Control using Vital Signs Data Prediction in Wireless Body Area Networks

Wireless Body Area Network (WBAN) is a wireless sensor network composed of sensors implanted under the skin or wearable sensors. These sensors are small and battery powered, making power efficiency an important and critical consideration. Data transmission is one of the most power consuming functions in the sensor node. This paper analyzes reducing data transmission, and hence power consumption, by predicting vital signs data instead of transmitting them all the time. We have focused on predicting the body vital signs like the temperature from other vital signs like the heart rate and the respiration rate. It is shown that the percentage of energy reduction depends on the rate of the prediction. Also, sending critical data in the alternating modes consumes more energy compared with the critical and the alternative prediction modes. It is shown that the critical alternating and critical transmission modes consumes more energy in Covid-19 patient compared to healthy person with MAE does not exceed 0.24. Finally, the multivariant model shows a great advantage in accuracy over univariant model. © 2022 IEEE.

An IoT-enabled Electronic Textile-based Flexible Body Sensor Network for Real-time Health Monitoring in Assisted Living during Pandemic

This paper presents a cost-effective and flexible electronic textile sensor with high sensitivity and fast response and demonstrates its versatile applications, including real-time measurements of finger kinematics, phonation, cough patterns, as well as subtle muscle movements (i.e., eye reflex). The sensor can discriminate between speech and cough patterns, thereby expanding its applications to COVID-19 detection, speech rehabilitation training, and human/machine interactions. A combination of different sensor data is essential to acquire clinically significant information. Therefore, a sensor array is interfaced with the LoRa communication protocol to establish an Internet of Things (IoT)-based electronic textile framework. The IoT integration allows remote monitoring of body kinematics and physiological parameters. Therefore, the proposed IoT-based framework holds the potential to provide real-time and continuous health monitoring to allow immediate intervention during this pandemic. © 2022 IEEE.