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{"Abstract":["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.\n\nParticipants 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.\n\nThe dataset includes:\n\n\n\n\n\nRaw and preprocessed IMU data from the chest, hands, and knees (quaternions, accelerometers, gyroscopes).\n\n\n\n\nFrame-level activity labels aligned with the protocol (target level for HAR).\n\n\n\n\nBiomarker data: heart rate and SpO₂ sampled at 0.5 Hz.\n\n\n\n\nDemographic metadata (age, height, weight, gender, BMI, body fat %, etc.).\n\n\n\n\nStep test heart rate (target variable for regression)."]}
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Indirect AI-Based Estimation of Cardiorespiratory Fitness from Daily Activities Using Wearables
Cardiorespiratory fitness is a predictor of long-term health, traditionally assessed through structured exercise protocols that require maximal effort and controlled laboratory conditions. These protocols, while clinically validated, are often inaccessible, physically demanding, and unsuitable for unsupervised monitoring. This study proposes a non-invasive, unsupervised alternative—predicting the heart rate a person would reach after completing the step test, using wearable data collected during natural daily activities. Ground truth post-exercise heart rate was obtained through the Queens College Step Test, which is a submaximal protocol widely used in fitness settings. Separately, wearable sensors recorded heart rate (HR), blood oxygen saturation, and motion data during a protocol of lifestyle tasks spanning a range of intensities. Two machine learning models were developed—a Human Activity Recognition (HAR) model that classified daily activities from inertial data with 96.93% accuracy, and a regression model that estimated post step test HR using motion features, physiological trends, and demographic context. The regression model achieved an average root mean squared error (RMSE) of 5.13 beats per minute (bpm) and a mean absolute error (MAE) of 4.37 bpm. These findings demonstrate the potential of test-free methods to estimate standardized test outcomes from daily activity data, offering an accessible pathway to infer cardiorespiratory fitness.
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- Award ID(s):
- 2439345
- PAR ID:
- 10661950
- Publisher / Repository:
- MDPI-Electronics
- Date Published:
- Journal Name:
- Electronics
- Volume:
- 14
- Issue:
- 15
- ISSN:
- 2079-9292
- Page Range / eLocation ID:
- 3081
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/electronics14153081
