Research

These days Medicinal services Condition has created science and learning dependent on Remote Detecting hub Technology situated. Patients are confronting a dangerous circumstance of unexpected end because of the particular explanation of heart issues and assault which is a direct result of nonexistence of good medicinal support to patients at the required time. With the increasing number of students in CUET, the doctors and medical facilities are not increasing. Only 6 doctors are assigned for about 3500 students of CUET and there are also shifting duty among them which means at a time not more than 2 doctors are present in CUET Medical Center. So, in the peak hour which is in the evening, there are a queue of patients in the medical center. In this work, a compact health monitoring system is developed where sick students from their room or anywhere can get access to the hospital facilities through IoT using the android app or our website. A student can upload his/her body temperature, SpO_2, Heart rate, Blood Hemoglobin (Hb) level, Anemic condition, Blood pressure, Blood sugar level using our device and an android smartphone or our website. Those data can be seen by a doctor over online through his smartphone or website and can treat remotely. This work has three parts such as hardware part, Simulation part, and website, android part. Arduino and sensors consist of hardware parts where using MATLAB, image processing, and machine learning techniques are used in the simulation part to predict Hemoglobin (Hb) and anemic condition along with this android app and website is also developed to upload these body parameters where doctors can get access remotely. The accuracy of Hemoglobin level prediction in Decision Tree method is 88.99% where the accuracy in anemic condition prediction is 82.61%. The cost of hardware parts is about 3160 taka which is cost effective. We were essentially planning and actualizing a framework where diverse body parameters of a patient is estimated. The body parameters are body temperature, beat rate, oxygen immersion in blood, Blood hemoglobin level expectation, frail condition forecast and Weight File (BMI). These body parameters can be uploaded to database through our system and our android app. On the opposite side doctor can monitor these body parameters remotely using the website that we designed. So, for measuring Body temperature, blood oxygen saturation and pulse rate we have used hardware components. For predicting Blood Hb level and detection of anemia we simulate and got result in MATLAB using different machine learning algorithms of regression and classification. These all parameter from hardware and software will be uploaded to the cloud for doctor’s monitoring.