Autosoft Journal

Online Manuscript Access

Feature Selection for Activity Recognition from Smartphone Accelerometer Data



We use the public Human Activity Recognition Using Smartphones (HARUS) data-set to investigate and identify the most informative features for determining the physical activity performed by a user based on smartphone accelerometer and gyroscope data. The HARUS data-set includes 561 time domain and frequency domain features extracted from sensor readings collected from a smartphone carried by 30 users while performing specific activities. We compare the performance of a decision tree, support vector machines, Naive Bayes, multilayer perceptron, and bagging. We report the various classification performances of these algorithms for subject independent cases. Our results show that bagging and the multilayer perceptron achieve the highest classification accuracies across all feature sets. In addition, the signal from gravity contains the most information for classification of activities in the HARUS data-set.



Total Pages: 9
Pages: 785-793


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways

Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Volume: 24
Issue: 4
Year: 2018

Cite this document


Bravo, José, Ramón Hervás, and Marcela Rodríguez, eds. "Ambient Assisted Living and Home Care." Lecture Notes in Computer Science (2012): n. pag. Crossref. Web.

Anguita D. J. Univers. Comput. Sci.

Bao, Ling, and Stephen S. Intille. "Activity Recognition from User-Annotated Acceleration Data." Pervasive Computing (2004): 1-17. Crossref. Web.

Bishop C.M. Neural Networks for Pattern Recognition

Demšar J. J. Mach. Learn. Res.

Fahim, Muhammad et al. "EFM: Evolutionary Fuzzy Model for Dynamic Activities Recognition Using a Smartphone Accelerometer." Applied Intelligence 39.3 (2013): 475-488. Crossref. Web.

Hall, Mark et al. "The WEKA Data Mining Software." ACM SIGKDD Explorations Newsletter 11.1 (2009): 10. Crossref. Web.

Jehn, Melissa et al. "Accelerometer-Based Quantification of 6-Minute Walk Test Performance in Patients With Chronic Heart Failure: Applicability in Telemedicine." Journal of Cardiac Failure 15.4 (2009): 334-340. Crossref. Web.

Kern, Nicky, Bernt Schiele, and Albrecht Schmidt. "Recognizing Context for Annotating a Live Life Recording." Personal and Ubiquitous Computing 11.4 (2006): 251-263. Crossref. Web.

Khan, Adil, Muhammad Siddiqi, and Seok-Won Lee. "Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones." Sensors 13.10 (2013): 13099-13122. Crossref. Web.

Kwapisz, Jennifer R., Gary M. Weiss, and Samuel A. Moore. "Activity Recognition Using Cell Phone Accelerometers." ACM SIGKDD Explorations Newsletter 12.2 (2011): 74. Crossref. Web.

Lara, Oscar D., and Miguel A. Labrador. "A Survey on Human Activity Recognition Using Wearable Sensors." IEEE Communications Surveys & Tutorials 15.3 (2013): 1192-1209. Crossref. Web.

Preece, S.J. et al. "A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data." IEEE Transactions on Biomedical Engineering 56.3 (2009): 871-879. Crossref. Web.

Ravi N. Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence - Volume 3, IAAI’05

Reyes Ortiz, Jorge Luis. "Smartphone-Based Human Activity Recognition." Springer Theses (2015): n. pag. Crossref. Web.

Reyes-Ortiz, Jorge-L. et al. "Transition-Aware Human Activity Recognition Using Smartphones." Neurocomputing 171 (2016): 754-767. Crossref. Web.

Shoaib, Muhammad et al. "A Survey of Online Activity Recognition Using Mobile Phones." Sensors 15.1 (2015): 2059-2085. Crossref. Web.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048