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

Biomedical Engineering Articles (54) Innovations and Technology for Physical Activity Education (537) Wearable Devices and Sensors (657) Fitness Trackers and Smart Pedometers/Accelerometers (492)

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 Popham 1 Author Orcid Image ;   Maximilien Burq 1 Author Orcid Image ;   Erin E Rainaldi 1 Author Orcid Image ;   Sooyoon Shin 1 Author Orcid Image ;   Jessilyn Dunn 2, 3, 4 Author Orcid Image ;   Ritu Kapur 1 Author Orcid Image
Article Authors Cited by Tweetations (6) 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 , US

2 Department of Biomedical Engineering , Duke University , Durham , NC , US

3 Department of Biostatistics & Bioinformatics , Duke University , Durham , NC , US

4 Duke Clinical Research Institute , Durham , NC , US

Corresponding Author: