Assessing Commercial Wearables in Predicting Physical Activity: A Case Study of Apple and Fitbit

No Thumbnail Available
Authors
Shadare, Ayodeji
Issue Date
2024
Degree
MSc in Data Analytics
Publisher
Dublin Business School
Rights
Abstract
This study explored the predictive accuracy of Apple Watch and Fitbit in tracking physical activity, employing advanced machine learning models, feature engineering, ensemble learning, and interpretable machine learning. Using Microsoft Power BI, an interactive dashboard was also constructed to analyze user demographics, physiological metrics, and activity patterns. The machine learning models, including Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, LightGBM, XGBoost, CatBoost, and Artificial Neural Network (ANN) were comprehensively evaluated using multiple metrics. Interpretability was enhanced through Shapley values, unravelling the contribution of features to classification results. Stacking models reveal insights into their performance compared to individual models. The result generally showed that the single LightGBM model was better compared to other models and stacking. Furthermore, the dashboard insights provide a detailed exploration of user engagement across different activities, revealing variations in heart rates, distances, and calorie expenditure. This study contributes unique insights into wearable technology.