Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification.

ABSTRACT:

Background

Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior "in the wild." Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms.

Method

Twenty-eight free-living women wore an Act

SUBMITTER: Nakandala S 

PROVIDER: S-EPMC8389343 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature