Classification of Sporting Activities Using Smartphone Accelerometers
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/13/4/5317 |
| _version_ | 1811299443792674816 |
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| author | Noel E. O'Connor David Monaghan Edmond Mitchell |
| author_facet | Noel E. O'Connor David Monaghan Edmond Mitchell |
| author_sort | Noel E. O'Connor |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T06:36:01Z |
| format | Article |
| id | doaj.art-dfdcd7fa7eee4b3aa13489518f2d0734 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:36:01Z |
| publishDate | 2013-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dfdcd7fa7eee4b3aa13489518f2d07342022-12-22T02:57:56ZengMDPI AGSensors1424-82202013-04-011345317533710.3390/s130405317Classification of Sporting Activities Using Smartphone AccelerometersNoel E. O'ConnorDavid MonaghanEdmond MitchellIn 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.http://www.mdpi.com/1424-8220/13/4/5317smartphoneclassificationsport |
| spellingShingle | Noel E. O'Connor David Monaghan Edmond Mitchell Classification of Sporting Activities Using Smartphone Accelerometers Sensors smartphone classification sport |
| title | Classification of Sporting Activities Using Smartphone Accelerometers |
| title_full | Classification of Sporting Activities Using Smartphone Accelerometers |
| title_fullStr | Classification of Sporting Activities Using Smartphone Accelerometers |
| title_full_unstemmed | Classification of Sporting Activities Using Smartphone Accelerometers |
| title_short | Classification of Sporting Activities Using Smartphone Accelerometers |
| title_sort | classification of sporting activities using smartphone accelerometers |
| topic | smartphone classification sport |
| url | http://www.mdpi.com/1424-8220/13/4/5317 |
| work_keys_str_mv | AT noeleo039connor classificationofsportingactivitiesusingsmartphoneaccelerometers AT davidmonaghan classificationofsportingactivitiesusingsmartphoneaccelerometers AT edmondmitchell classificationofsportingactivitiesusingsmartphoneaccelerometers |