Dobbins, CM and Rawassizadeh, R (2018) Towards Clustering of Mobile and Smartwatch Accelerometer Data for Physical Activity Recognition. Informatics, 5 (2). ISSN 2227-9709

Preview

Text informatics-05-00029-1.pdf - Published Version Available under License Creative Commons Attribution. Download (7MB) | Preview

Abstract

Mobile and wearable devices now have a greater capability of sensing human activity ubiquitously and unobtrusively through advancements in miniaturization and sensing abilities. However, outstanding issues remain around the energy restrictions of these devices when processing large sets of data. This paper presents our approach that uses feature selection to refine the clustering of accelerometer data to detect physical activity. This also has a positive effect on the computational burden that is associated with processing large sets of data, as energy efficiency and resource use is decreased because less data is processed by the clustering algorithms. Raw accelerometer data, obtained from smartphones and smartwatches, have been preprocessed to extract both time and frequency domain features. Principle component analysis feature selection (PCAFS) and correlation feature selection (CFS) have been used to remove redundant features. The reduced feature sets have then been evaluated against three widely used clustering algorithms, including hierarchical clustering analysis (HCA), k-means, and density-based spatial clustering of applications with noise (DBSCAN). Using the reduced feature sets resulted in improved separability, reduced uncertainty, and improved efficiency compared with the baseline, which utilized all features. Overall, the CFS approach in conjunction with HCA produced higher Dunn Index results of 9.7001 for the phone and 5.1438 for the watch features, which is an improvement over the baseline. The results of this comparative study of feature selection and clustering, with the specific algorithms used, has not been performed previously and provides an optimistic and usable approach to recognize activities using either a smartphone or smartwatch.

Item Type:	Article
Uncontrolled Keywords:	clustering; smartwatch; smartphone; activity recognition; feature selection
Subjects:	G Geography. Anthropology. Recreation > GV Recreation Leisure Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Computer Science & Mathematics
Publisher:	MDPI
Date Deposited:	13 Jun 2018 09:12
Last Modified:	04 Sep 2021 10:23
DOI or ID number:	10.3390/informatics5020029
URI:	https://researchonline.ljmu.ac.uk/id/eprint/8833

View Item