Open AccessThe CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study
Author(s) -
Mikael Anne Greenwood-Hickman,
Supun Nakandala,
Marta M. Jankowska,
Dori E. Rosenberg,
Fatima TuzZahra,
John Bellettiere,
Jordan Carlson,
Paul R. Hibbing,
Jingjing Zou,
Andrea Z. LaCroix,
Arun Kumar,
Loki Natarajan
Publication year - 2021
Publication title -
medicine and science in sports and exercise
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.703
H-Index - 224
eISSN - 1530-0315
pISSN - 0195-9131
DOI - 10.1249/mss.0000000000002705
Subject(s) - sitting , accelerometer , physical medicine and rehabilitation , medicine , inclinometer , physical therapy , computer science , cartography , geography , pathology , operating system
Sitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method.