Statistical and Exploratory Data Analysis of Stress and Physical Activity Measurement through Smart Watches

This research is dedicated to harnessing the power of statistical and analytical techniques to estimate stress and physical activity levels using data from smart watches and health apps. The research revolves around in analyzing a data set obtained from willing volunteers who shared their data from the iPhone Health App (Apple Health) and Zepp Life App (MI Health Band). The overall goal of this study was to gather valuable insights from the data, develop a statistical framework for evaluating health-related data, and make comparisons at individual and group levels to predict stress due to physical activity and resting periods. Specifically, this study represents a concerted effort to bridge the gap between engineering and health research using innovative methods of data analysis. By leveraging the vast reservoir of information generated by smartwatches, we seek to shed light on the complex relationship between stress and physical activity, and offer potential benefits for individuals provides extensive welfare and public health services. The outcome derived from this study is that the people with either extremely skewed recorded data, or consistently higher or lower bound data, are at risk of greater health illnesses, while the people whose recorded data is consistent and not touching upper or lower bounds, are considered healthy.