The world’s population is increasing since year 1950 till now. The large number of population with age between 25 and 29 implied the importance of health care services to maintain this population’s good health. However, there are a lot of health measuring devices only measure one health parameter from each person. This is very inconvenience to most of the users. Another problem encountered is that there is a large number of health data that are not analysed by the system. Therefore, the purpose of this research is to develop a data acquisition system that consists of three sensors, which are temperature sensor, pulse oximeter sensor and heart rate sensor. Besides, this project also develops Support Vector Machine (SVM) based machine learning algorithm to monitor health condition. All the sensors will measure respective reading and read by Arduino microcontroller. The reading will then transfer to Raspberry Pi 3 via serial communication for health prediction using machine learning. A classification model is derived from 240 training data and tested with 60 testing data. The classification model gives an overall accuracy of 93.33%. While looking at user’s accuracy on each class, all class except two classes give 100% accuracy. However, both ROC of these two classes are 0.998, which are still high. Therefore, the classification model is good and can be used to predict health condition.
Health monitoring Data acquisition SVM
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United Nations, D.o.E.a.S.A., Population Division, World Population Prospects: The 2017 Revision, Key Findings and Advance Tables (2017)Google Scholar
Görs, M., et al.: Design of an advanced telemedicine system for remote supervision. IEEE Syst. J. 10(3), 1089–1097 (2016)CrossRefGoogle Scholar
Romero-Aroca, P., et al.: Importance of telemedicine in diabetes care: relationships between family physicians and ophthalmologists. World J. Diabet. 6(8), 1005–1008 (2015)CrossRefGoogle Scholar
Sundaravadivel, P., et al.: Everything you wanted to know about smart health care: evaluating the different technologies and components of the Internet of Things for better health. IEEE Consum. Electron. Mag. 7(1), 18–28 (2018)CrossRefGoogle Scholar
Andreu-Perez, J., et al.: From wearable sensors to smart implants—toward pervasive and personalized healthcare. IEEE Trans. Biomed. Eng. 62(12), 2750–2762 (2015)CrossRefGoogle Scholar
Soh, P.J., et al.: Wearable wireless health monitoring: current developments, challenges and future trends. IEEE Microw. Mag. 16(4), 55–70 (2015)CrossRefGoogle Scholar
Obermeyer, Z., Emanuel, E.J.: Predicting the future—big data, machine learning, and clinical medicine. New Engl. J. Med. 375(13), 1216–1219 (2016)CrossRefGoogle Scholar
Vizer, L.M., Sears, A.: Classifying text-based computer interactions for health monitoring. IEEE Pervasive Comput. 14(4), 64–71 (2015)CrossRefGoogle Scholar