This paper is in the following e-collection/theme issue:

Development of Novel Medical Devices and Innovations for Existing Devices (83) Innovations and Technology for Physical Activity Education (756) Wearable Devices and Sensors (903) Fitness Trackers and Smart Pedometers/Accelerometers (571)

Published on in Vol 8 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43726, first published .
An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study

An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study

An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study

Authors of this article:

Sara Popham1 Author Orcid Image ;   Maximilien Burq1 Author Orcid Image ;   Erin E Rainaldi1 Author Orcid Image ;   Sooyoon Shin1 Author Orcid Image ;   Jessilyn Dunn2, 3, 4 Author Orcid Image ;   Ritu Kapur1 Author Orcid Image
Article Authors Cited by (6) Tweetations (10) Metrics

Sara Popham   1 , PhD ;   Maximilien Burq   1 , PhD ;   Erin E Rainaldi   1 , MSc ;   Sooyoon Shin   1 , PhD ;   Jessilyn Dunn   2, 3, 4 , PhD ;   Ritu Kapur   1 , PhD

1 Verily Life Sciences, South San Francisco, CA, United States

2 Department of Biomedical Engineering, Duke University, Durham, NC, United States

3 Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States

4 Duke Clinical Research Institute, Durham, NC, United States

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