Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data.

ABSTRACT: Few methods for classifying physical activity from accelerometer data have been tested using an independent dataset for cross-validation, and even fewer using multiple independent datasets. The aim of this study was to evaluate whether unsupervised machine learning was a viable approach for the development of a reusable clustering model that was generalisable to independent datasets. We used two labelled adult laboratory datasets to generate a k-means clustering model. To assess its generalised application, we applied the stored clustering model to three independent labelled datasets: two laboratory and one free-living. Based on the development labelled data, the ten clusters were collapsed into four activity categories: sedentary, standing/mixed/slow ambulatory, brisk ambulatory, and runn

SUBMITTER: Jones P 

PROVIDER: S-EPMC6832944 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature