Daily Activities Wearable Dataset for Cardiorespiratory Fitness Estimation

This dataset was collected as part of the study "Indirect AI-Based Estimation of Cardiorespiratory Fitness from Daily Activities Using Wearables." It contains synchronized sensor data and physiological measurements from participants performing a structured sequence of daily activities. The dataset is designed to support research in human activity recognition (HAR) and indirect estimation of cardiorespiratory fitness, particularly through heart rate regression after a submaximal step test. Participants wore a combination of inertial measurement units (IMUs) and biometric sensors in a controlled indoor environment. Each session followed a predefined activity protocol interleaving rest and effort, and a 3-minute step test to elicit a measurable cardiorespiratory response. The dataset includes: Raw and preprocessed IMU data from the chest, hands, and knees (quaternions, accelerometers, gyroscopes). Frame-level activity labels aligned with the protocol (target level for HAR). Biomarker data: heart rate and SpO₂ sampled at 0.5 Hz. Demographic metadata (age, height, weight, gender, BMI, body fat %, etc.). Step test heart rate (target variable for regression).