Next Article in Journal
Open-Source Colorimeter
Previous Article in Journal
A PARALIND Decomposition-Based Coherent Two-Dimensional Direction of Arrival Estimation Algorithm for Acoustic Vector-Sensor Arrays
Open AccessArticle

Classification of Sporting Activities Using Smartphone Accelerometers

Centre for Sensor Web Technologies, Dublin City University, Dublin D9, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2013, 13(4), 5317-5337; https://doi.org/10.3390/s130405317
Received: 26 February 2013 / Revised: 8 April 2013 / Accepted: 11 April 2013 / Published: 19 April 2013
(This article belongs to the Section Physical Sensors)
In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach. View Full-Text
Keywords: smartphone; classification; sport smartphone; classification; sport
Show Figures

Graphical abstract

MDPI and ACS Style

Mitchell, E.; Monaghan, D.; O'Connor, N.E. Classification of Sporting Activities Using Smartphone Accelerometers. Sensors 2013, 13, 5317-5337. https://doi.org/10.3390/s130405317

AMA Style

Mitchell E, Monaghan D, O'Connor NE. Classification of Sporting Activities Using Smartphone Accelerometers. Sensors. 2013; 13(4):5317-5337. https://doi.org/10.3390/s130405317

Chicago/Turabian Style

Mitchell, Edmond; Monaghan, David; O'Connor, Noel E. 2013. "Classification of Sporting Activities Using Smartphone Accelerometers" Sensors 13, no. 4: 5317-5337. https://doi.org/10.3390/s130405317

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Search more from Scilit
 
Search
Back to TopTop