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

Bioinformatics, genomics, tools and databases (17) Machine Learning (1465) Fitness Trackers and Smart Pedometers/Accelerometers (561) Innovations and Technology for Physical Activity Education (742)

Published on in Vol 3, No 1 (2022): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38512, first published .
Monitoring Physical Behavior in Rehabilitation Using a Machine Learning–Based Algorithm for Thigh-Mounted Accelerometers: Development and Validation Study

Monitoring Physical Behavior in Rehabilitation Using a Machine Learning–Based Algorithm for Thigh-Mounted Accelerometers: Development and Validation Study

Monitoring Physical Behavior in Rehabilitation Using a Machine Learning–Based Algorithm for Thigh-Mounted Accelerometers: Development and Validation Study

Authors of this article:

Frederik Skovbjerg1 Author Orcid Image ;   Helene Honoré1 Author Orcid Image ;   Inger Mechlenburg2 Author Orcid Image ;   Matthijs Lipperts3 Author Orcid Image ;   Rikke Gade4 Author Orcid Image ;   Erhard Trillingsgaard Næss-Schmidt1, 2 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Honoré H, Skovbjerg F, Pedersen A, Mechlenburg I, Nielsen J. Exploring Physical Activity During the Discharge Transition Phase in People With Acquired Brain Injury—An Observational Study. Archives of Rehabilitation Research and Clinical Translation 2023;5(1):100247 View
  2. Lendt C, Hansen N, Froböse I, Stewart T. Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry. International Journal of Behavioral Nutrition and Physical Activity 2024;21(1) View