Smartwatch Health Analytics: A Data-Driven Study of Physiological and Behavioral Metrics
Abstract
With the proliferation of wearable devices, massive volumes of health-related data are generated daily. This paper presents an analytical exploration of physiological metrics obtained from smartwatches, including heart rate, oxygen saturation, sleep duration, and stress levels. Using a cleaned dataset of anonymized users, we evaluate patterns, identify correlations, and compare behaviors across activity levels. Through exploratory data analysis and machine learning techniques, we demonstrate the potential of smartwatches in monitoring individual and population-level health trends.
Keywords
Download Options
Introduction
The widespread use of smartwatches has enabled continuous, non-invasive monitoring of vital health indicators. These devices track physical activity, cardiovascular health, sleep quality, and more. With regular data collection, they offer an opportunity for real-time personal health monitoring and population-wide health analytics.
This study leverages smartwatch data to investigate the distribution and interrelation of health indicators, emphasizing how activity levels relate to stress, sleep, and heart rate.
Conclusion
This study illustrates the immense potential of smartwatch data in health analytics. By carefully preprocessing and analyzing user metrics, we identify patterns useful for preventive health strategies. Smartwatches offer not just convenience, but an opportunity for long-term population health monitoring and personalized care.
Future work includes integrating contextual data like diet or mood, and using predictive models to forecast health events.