Sleep Quality Prediction From Wearable Device Data: A Comprehensive Analysis and Model Comparison

This study explores machine learning techniques for predicting sleep quality from wearable device data. It compares classification and regression approaches, including Random Forest, Gradient Boosting, and deep learning models like RNNs and CNNs. Results highlight the effectiveness of these models and offer insights into optimal algorithms and feature selection for accurate sleep quality prediction.