Dataset description 16 participants wore an Actigraph w-GT3X on their right hip together with two consumer-based wearables, a Polar Vantage (Polar Electro Oy, Kempele, Finland) watch and an Oura (Oura Health Oy, Oulo, Finland]) ring. This data, as summarized in this repository, has been used to validate an algorithm to detect time in bed. For more information, see the publication's abstract below.

This dataset consists of 20 files: "00_ReadMe.txt", "calibration_error.txt", "data_protection_impact_assessment.pdf", "wearable_data.csv" and 16 raw acceleration data files ("participant00.csv to participant15.csv").

"00_ReadMe.txt": Contains a detailed information about the dataset "calibration_error.txt": Contains the estimated calibration errors, correction coefficients, and success information for the 16 raw acceleration data files. "data_protection_impact_assessment.pdf": sets out the legal and ethical grounds for open publication of the dataset. "wearable_data.csv": Contains daily sleep duration values from a Polar watch and the Oura ring for the 16 participants Each of the 16 raw acceleration data files (participantXX.csv") contains the triaxial raw acceleration data at a 100hz sampling frequency.

Article abstract Accelerometers are frequently used to assess physical activity in large epidemiological studies. They can monitor movement patterns and cycles over several days under free-living conditions and are usually either worn on the wrist or the hip. While wrist-worn accelerometers have been frequently used to additionally assess sleep and time in bed behavior, hip-worn accelerometers have been widely neglected for this task due to their primary focus on physical activity. Here, we present a new method with the objective to identify the time in bed to enable further analysis options for large-scale studies using hip-placement like time in bed or sedentary time analyses. We introduced new and accelerometer specific data augmentation methods, such as mimicking a wrongly worn accelerometer, additional noise, and random croping, to improve training and generalization performance. Subsequently, we trained a neural network model on a sample from the population-based Tromsø Study and evaluated it on two additional datasets. Our algorithm achieved an accuracy of 94% on the training data, 92% on unseen data from the same population and comparable results to consumer-wearable data obtained from a demographically different population. Generalization performance was overall good, however, we found that on a few particular days or participants, the trained model fundamentally over- or underestimated time in bed (e.g., predicted all or nothing as time in bed). Despite these limitations, we anticipate our approach to be a starting point for more sophisticated methods to identify time in bed or at some point even sleep from hip-worn acceleration signals. This can enable the re-use of already collected data, for example, for longitudinal analyses where sleep-related research questions only recently got into focus or sedentary time needs to be estimated in 24h wear protocols.

Polar M430, 3.2.10

Oura Model 2P, 1.91.1

ActiGraph wGT3X-BT, 1.9.2

ActiLife, 6.13.3