Towards Clustering of Mobile and Smartwatch Accelerometer Data for Physical Activity Recognition
Journal Title: Informatics - Year 2018, Vol 5, Issue 2
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.
Authors and Affiliations
Chelsea Dobbins and Reza Rawassizadeh
Keywords
Related Articles
- EP ID EP44131
- DOI https://doi.org/10.3390/informatics5020029
- Views 244
- Downloads 0
Identifying Opportunities to Integrate Digital Professionalism into Curriculum: A Comparison of Social Media Use by Health Profession Students at an Australian University in 2013 and 2016
Social media has become ubiquitous to modern life. Consequently, embedding digital professionalism into undergraduate health profession courses is now imperative and augmenting learning and teaching with mobile technol...
Using Introspection to Collect Provenance in R
Data provenance is the history of an item of data from the point of its creation to its present state. It can support science by improving understanding of and confidence in data. RDataTracker is an R package that coll...
Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors
Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which...
Fitness Activity Recognition on Smartphones Using Doppler Measurements
Quantified Self has seen an increased interest in recent years, with devices including smartwatches, smartphones, or other wearables that allow you to monitor your fitness level. This is often combined with mobile apps...
Acknowledgement to Reviewers of Informatics in 2016
The editors of Informatics would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016. We greatly appreciate the contribution of expert reviewers, which is crucial to the...