Machine Learning for Wearables in Biomedical Research

Wearable devices generate vast amounts of continuous physiological data, offering unprecedented insights into human health. This text-based course guides you through the foundational concepts and modern machine learning techniques needed to transform raw sensor data into meaningful biomedical discoveries.\n\nYou will transition from understanding basic sensor outputs to designing structured machine learning pipelines for large-scale health studies. You will learn how to handle missing data, extract digital biomarkers, and train predictive models using industry-standard analysis techniques.\n\nWhat you'll learn:\n- Understand the fundamental types of wearable sensor data, including accelerometry and heart rate monitoring.\n- Process and clean large-scale time-series datasets using modern, efficient data-wrangling workflows.\n- Extract engineered features and digital biomarkers to capture physical activity and sleep patterns.\n- Apply machine learning algorithms to classify behaviors and predict health outcomes.\n- Evaluate model performance robustly while accounting for participant-level data splits to prevent leakage.\n- Address ethical, privacy, and data standardization challenges in biomedical research.\n\nThe course begins with essential terminology and the unique characteristics of health sensor data before moving into step-by-step conceptual pipelines. You will read through detailed explanations of data preprocessing, feature extraction, and model validation techniques tailored for clinical and epidemiological research.\n\nThis course is designed for clinical researchers, data analysts, and students who are new to machine learning and want to analyze health sensor data. No advanced mathematical background is required to follow along.\n\nStart exploring the potential of health informatics and wearable technology today.