Patient Health Monitoring and Controlling System using Internet-of-Things (IoT)

Abstract

Abstract:IoT is a platform which offers various applications in different fields. IoT provides an important contribution in the field of healthcare, its principles and properties are already being applied to improve access to healthcare, improve the quality of healthcare and most importantly reduce the cost of system. The technology is used for gathering, analyzing and transmitting data in the IoT continues to new innovative healthcare applications and systems. Wireless devices are involved in medical area with a wide range of capability. To monitor the patient's health details in periodic interval is necessary in existing technologies. To overcome this we have changed recent wireless sensor technologies. In general different sensors are used to gather patient's medical information without being injecting inside the body by this we are achieving monitoring and data gathering of patients. There is no need for doctor to visit patient periodically. IoT provides new applications as well as efficient systems which helps to make an integrated healthcare system. The system provides better capability, healthcare system and costs are also reduced. Treatment outcomes are also improved.

Remote ECG signal monitoring and classification based on Arduino with AD8232 sensor

“…Pre-processing is the final stage of preparation of the signal to be input to the classifier. As we mention previously , the sampling rate should be 184 sample , so the signal should have this number of sample, also the signal should be normalized , because the signal is measured in µv, so it must be converted to mv, all this preprocessing is shown in figure (19). In the proposed classification CNN model, We are used different pooling layer to extract the features of convolution layers in the proposed CNN model and the results in terms of accuracy are described in Table (1), which shows that HGT method is achieved best result (94.94%) verses (93.97%) for GWT pooling methods [13,14] , (94.58) for (wt+ max )method, while the other performance metrics are shown in Table (2).The best results are satisfied with ( HGT)method [24], also it is achieved( 94.56%), (94.56% ) and (5.06) for sensitivity specificity and error rate (ERR) respectively.…”

“…This advantage can recover AD8232 quickly, and take valid measurements soon after connecting electrodes to the subject. Performance for the A grade models is Rated temperature range from zero °C to 70°C and working with temperature range from -40°C to 85°C, the description of this device is shown in figure (3) [18][19][20].…”

“…Baseline is low frequency can be arises from breathing, skeletal muscle activity generate muscle artifact, while the change in the electrode skin impedance will cause electrode motion noise [21]. Unfortunately, these noise can reduce the accuracy of diagnosis, so the raw signal should be denoised to reduce the effect of the noise, which can be satisfied by using wavelet transform and applying threshold as shown in figure (6), where the wavelet transform is applied to isolate the frequency band, then high frequency components of the signal is applied to threshold to reduce the noise which is contended as high frequency [22].…”

“…The sensed data will be transmitted to Web server , from this web server, anyone can monitor status of patient at anywhere of the world, later data are downloaded to be used as input Aqeel M.Hamad alhussainy (1) , Ammar D. Jasim (2) Remote ECG signal monitoring and classification based on Arduino with AD8232 sensor to classification system. We are used Nodemcu esp8266 microcontroller, which is used MQTT connection in Webserver(Ubidots cloud), then the data is transmitted according to the programmed Wi-Fi and specified allocated IP address [5,6].…”