Sensors, Vol. 19, Pages 4504: Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data

Sensors, Vol. 19, Pages 4504: Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data Sensors doi: 10.3390/s19204504 Authors: Petra Jones Evgeny M. Mirkes Tom Yates Charlotte L. Edwardson Mike Catt Melanie J. Davies Kamlesh Khunti Alex V. Rowlands 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 running. The percentages of each activity type contained in these categories were 89%, 83%, 78%, and 96%, respectively. In the laboratory independent datasets, the consistency of activity types within the clusters dropped, but remained above 70% for the sedentary clusters, and 85% for the running and ambulatory clusters. Acceleration features were similar within each cluster across samples. The clusters ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research