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Assessment of Physical Activity Intensity with Accelerometers and Oxygen Consumption
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The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-living

Veronika Potter1, Hoan Tran1, Daniel Mobley2

  • 1Northeastern University, USA.

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces the PAAWS R1 dataset, featuring multimodal sensor data for accurately recognizing physical activities and sleep patterns in real-world settings. This resource aims to improve human activity recognition algorithms for better health research and mobile health interventions.

Keywords:
AccelerometerActivity RecognitionDatasetPhysical ActivitySedentary BehaviorSleepWearables

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Area of Science:

  • Human Activity Recognition
  • Wearable Sensor Technology
  • Digital Health

Background:

  • Poor sleep and sedentary behaviors are linked to chronic diseases and reduced quality of life.
  • Wearable sensors can monitor physical activity, sedentary behavior, and sleep in free-living conditions.
  • Current human activity recognition algorithms struggle with real-world data due to lab-based training.

Purpose of the Study:

  • To introduce the PAAWS R1 dataset, a multimodal, multi-sensor resource for human activity recognition.
  • To provide a dataset enabling direct comparison of algorithms across different collection protocols and free-living conditions.
  • To facilitate the development of robust algorithms for health research and mobile computing interventions.

Main Methods:

  • Collected ~4 hours of semi-naturalistic activities from 252 individuals.
  • Collected ~7 days of 24-hour free-living activities from 20 adults.
  • Annotated waking activities with second-by-second video ground-truth labels and sleep stages from PSG data.

Main Results:

  • The PAAWS R1 dataset includes diverse, real-world activity and sleep data.
  • High-resolution annotations capture realistic activity transitions and sleep stages.
  • The dataset supports direct algorithmic comparisons across varied collection settings.

Conclusions:

  • The PAAWS dataset is a valuable resource for advancing human activity recognition.
  • It enables the development of more robust algorithms for free-living conditions.
  • This work supports improved health research and the creation of novel mobile health applications.