Comprehensive Health Tracking Through Machine Learning and Wearable Technology

Abstract

The accurate interpretation of data from wearable devices is paramount in advancing personalized healthcare and disease prevention. This study explores the application of machine learning techniques to improve the interpretation of health metrics from wearable technology, focusing on heart rate and activity prediction. The study conducts a device-wise comparison of data from popular devices, namely the Apple Watch and Fitbit, using both tree-based and boosting algorithms. The outcome of the experiment shows that the Random Forest model is a better predictor for heart rate, with the lowest error rate across devices and a prediction accuracy of 98% on the combined dataset. Conversely, the classification result for activity prediction showed that all models used have better accuracy with Fitbit data, and accuracy drops with Apple Watch data. The Random Forest achieves a consistent performance of 87% for accuracy and F1 score on the combined data. However, after cross-validated hyperparameter tuning, this result on the combined dataset is superseded by the boosted models, with both Gradient Boosting and XGBoost achieving the same level of performance (90%) across metrics.

Received: 10 June 2024 | Revised: 18 December 2024 | Accepted: 19 February 2025 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study. The code—and parameters for the different algorithms—can be made available by contacting the authors.

Author Contribution Statement

Abusufyan Yusuf: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Tareq Al Jaber: Conceptualization, Data curation, Writing – review & editing, Supervision. Neil Gordon: Conceptualization, Data curation, Writing – review & editing.