A Chronic Kidney Disease (CKD) monitoring system was proposed for early detection of cardiovascular disease (CVD) and anemia using Fuzzy Logic. To determine the heart rate and blood oxygen saturation, the proposed model was simulated using MATLAB and Simulink to handle ECG and PPG inputs. The Pan-Tompkins method was used to determine the heart rate, while the Takuo Aoyagi algorithm was used to assess blood oxygen saturation levels. The findings show that the ECG recorded using the CKD model has all of the characteristics of a typical ECG wave cycle, but with reduced signal degradation in the 0.8-1.3mV region. The heart rate signal processing yielded findings between 78 and 83 beats per minute is within the range of the supplied heart rate. Takuo Aoyagi's pulse oximeter simulation generated the same findings. For real-time verification, the proposed model was implemented in hardware using ESP8266 32-bit microcontroller with IoT integration via Wireless Fidelity for data storage and monitoring. In comparison with the Fuzzy Logic simulation done on MATLAB and Simulink, the CKD monitoring device has 100% accuracy in patient status detection. The CKD monitoring system has an overall accuracy of 99% in comparison with a commercial fingertip pulse oximeter.

This paper describes the simulation done on a low-cost biosensor interface controller for Chronic Kidney Disease (CKD) monitoring system using Internet of Things (IoT). Healthcare monitoring systems are devices that keep track of human activities and health conditions using biosensors. The developed monitoring system will aid in chronic disease patients for early detection of prevailing diseases. Early prevention can be done by monitoring the electrocardiogram (ECG). However, ECG signals typically contain contaminants that cause inaccuracy in the ECG signals produced and difficulty in diagnosing the heart’s activity. The objective is to design and simulate a system to perform pre-processing of ECG signals to prevent ECG measurements from signal contamination. Next, to calculate the heart rate using filtered ECG signals and the Pan-Tompkins algorithm. The simulation was done on MATLAB and Simulink by generating pre-recorded ECG signals that will be pre-processed to obtain viable results when compared to a normal ECG cycle wave. The results show that the filtered ECG produced has all the elements of a normal ECG cycle wave with less signal contamination within the range of 0.8 – 1.3mV. The filtered ECG signals were processed for QRS peak detection to obtain the heart rate. Results show that the heart rate displayed was within the range of the pre-recorded heart rate which is 79 – 82 beats per minute (BPM). The QRS peaks detected were also identical to the results from the Pan-Tompkins algorithm.

Rapid transmission of the coronavirus disease via droplets and particles has led to a global pandemic. Expeditious detection of SARS-Cov-2 RNA in the environment is attainable by using Bio-FET sensors. This work proposes a Bio-FET sensor interface module with IoT implementation to amplify signals from a Bio-FET for SARS-Cov-2 detection and monitoring. The sensor interface module was programmed to read the signals using a micro-controller and process information to determine the presence of SARS-Cov-2. The proposed Bio-FET sensor interface module was also set to transmit data to the Cloud via W-Fi to be stored and displayed on a dashboard. The prototype Bio-FET sensor interface module was simulated in PSpice for signal amplification, and hardware implementation has been done by using low-cost components for data transmission to the Cloud. The hardware consists of an AD620 instrumentation amplifier module, voltage sensor module, Neo-6m GPS sensor module, an OLED display, and an ESP8266-32 bit micro-controller. The results of both the simulation and the hardware implementation are similar. The emulated negative and positive Bio-FET signal outputs were successfully amplified from 15.9mV and 45.8mV to 1.59V and 4.58V, respectively, using an AD620 instrumentation amplifier. The gathered location, time, date, output voltage, and SARS-Cov-2 presence results were successfully stored and displayed on the Cloud dashboard.