Health Management For Users From Wearable Device Data By Applying Machine Learning Algorithms

The growing adoption of wearable devices like fitness bands and smart watches from brands like Apple, Boat, and Noise, has enabled the continuous tracking of health parameters like physical activities – (Walking, Running) , heart rate, sleep patterns. This research focuses on how machine learning (ML) algorithms can be used to predict an individual's health status using data over 2 months from 30 individuals across various locations. The data, which varies in format depending on the device used, undergoes pre-processing to standardize and normalize the inputs, ensuring compatibility for ML analysis. Various ML algorithms, including Regression ,Decision Tree, support vector machines, and, are applied to uncover health trends, identify the health risks associated , and help in developing a personalized insights. The diverse set of participants and the extended data collection period allows for the identification of long-term health patterns and offer more accurate health predictions. The findings demonstrate the potential of leveraging wearable devices and machine learning to deliver personalized, data-driven health insights, contributing to proactive healthcare and improved fitness management.