earticle

영어

Modern technology is developing by leaps and bounds, and more and more people begin using wearable technology devices. Recently, users have been using this kind of devices such as Fitbit, Apple Watch and Samsung wrist trackers so as to keep track of their health data such as consumed calories, running miles and steps, and even sleeping time. Many users wear their devices nearly 24/7, providing a thorough weekly health analysis in the devices’ applications installed in their mobile phones. However, few people really use wearable devices to diagnose or identify common diseases which can be captured by the fluctuations or major changes in data captured by the devices. Hence, integrating with machine learning technology, we attempt to figure out a solution to detect and diagnose some diseases based on the daily health data collected by wearable devices. Aiming at this, we collected data and experimented using a classification-based machine learning method, namely Support Vector Machine, to simulate a verisimilar ambient to monitor certain users’ health conditions.