IOT Based Real-Time Patients Health Monitoring System Zhaoqi Song, Wei Xia, Xinyue Gui, Manquan Fang Advisor: Xingguo Xiong Department of Electrical Engineering, University of Bridgeport, Bridgeport, CT 06604 Abstract This paper has two parts. The first part is using the IOT to measure and record some basic signs. The temperature sensor is connected to a LilyPad Arduino Board to measure patients’ real-time body temperature. A wearfit watch is sticked on the clothes to measure patient's real-time blood pressure and heartbeat. Then the software Arduino will be used to connect the board with computer by Wifi module. After that, the data collected by the sensors will be transferred into computers as excel file. The second part is doing data analysis to predict if the patient will relapse by using Naive Bayesian Algorithm. Part#2. Software Design(Data Analysis) We have downloded a dataset from This dataset records 306 instances with 6 attributes: 1.Age of patient at time of operation(numerical) 2.Patients’ body temperature(numerical) 3.Patient’s heartbeat(numerical) 4.Patient’s systolic pressure(numerical) 5.Patient’s diastolic pressure(numerical) 6. If the patient relapse within one week(1=the patient relapse within one week , 0=the patient doesn’t relapse within 1 week) We choose this data set because there are obviously two classifications, 1 signifies the patient the patient relapse within one week, 0 signifies the patient doesn’t relapse within 1 week. Meantime, the attributes are comparatively independent of each other. Than I use csv.read to import the data set and Naive Bayesian algorithm to train the model. Pivture 4. Result of prediction The dataset is seperated to train set(514 samples) and test set(254 samples), the accuracy of the model is 76.77%, Recall=82.58%, Precision=84.0%. Introduction Nowadays, people pay more attention to their healthy condition. Especially for the postoperative patients, to cut cost and leave the medical resources for those people who need urgent help, they can choose to return home to recuperate. However, the recurrence rate may be high for some diseases, so we want to design an IOT Based Real-Time Patients Health Monitoring System to monitor the healthy condition of these patients so that they will get timely treatment. Part#1. Arduino Board This circuit contains an Arduino board, one 5V power, one temperature sensor, two orange LEDs, two green LEDs. Picture 1. Circuit of Arduino The temperature sensor is used to monitor the body temperature of patients. When body temperature is normal(36.0℃<=T<=37.3℃), the power can be supplied to green LEDs, and they can light up. When body temperature is abnormal(T<36.0℃ || T>37.3℃), the power can be supplied to orange LEDs, and they can light up. So this device can be used as an alarm to notify if the patient has good healthy condition. Meanwhile, the two orange LEDs and two green LEDs are separately in parallel connection. Thus, when one LED break down, the other one can still work. Picture 2. Code of Arduino This code reads the input of the temperature sensor, converts it to Farenheit and Celsius and prints to the Serial Monitor. Picture 3. Main programming of source code Conclusion and Future Work By evaluating the model based on the accuracy, recall and precision, the age, real-time body temperature, blood pressure and heart beat can predict if the patient will replase to some extent. In the future, we would like to improve our project from two aspects. Firstly, by observing more kinds of body indexes, we can get a more accurate prediction. Secondly, we can not only use real-time data, but also combine the data of several days to predict result. This is like a time-series model. The data of different time has different effects to the final result. Thus, we can also use LSTM(Long Short Term Memory)to construct a more accurate model. References 1. LilyPad Temperature Sensor Hookup Guide, learn.sparkfun.com/ tutorials/lilypad-temperature-sensor-hookup-guide. 2. Xingjian, S. H. I., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in neural information processing systems (pp. 802-810).