Towards unsupervised physical activity recognition using smartphone accelerometers View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-04

AUTHORS

Yonggang Lu, Ye Wei, Li Liu, Jun Zhong, Letian Sun, Ye Liu

ABSTRACT

The development of smartphones equipped with accelerometers gives a promising way for researchers to accurately recognize an individual’s physical activity in order to better understand the relationship between physical activity and health. However, a huge challenge for such sensor-based activity recognition task is the collection of annotated or labelled training data. In this work, we employ an unsupervised method for recognizing physical activities using smartphone accelerometers. Features are extracted from the raw acceleration data collected by smartphones, then an unsupervised classification method called MCODE is used for activity recognition. We evaluate the effectiveness of our method on three real-world datasets, i.e., a public dataset of daily living activities and two datasets of sports activities of race walking and basketball playing collected by ourselves, and we find our method outperforms other existing methods. The results show that our method is viable to recognize physical activities using smartphone accelerometers. More... »

PAGES

10701-10719

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11042-015-3188-y

DOI

http://dx.doi.org/10.1007/s11042-015-3188-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041706779


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45 schema:description The development of smartphones equipped with accelerometers gives a promising way for researchers to accurately recognize an individual’s physical activity in order to better understand the relationship between physical activity and health. However, a huge challenge for such sensor-based activity recognition task is the collection of annotated or labelled training data. In this work, we employ an unsupervised method for recognizing physical activities using smartphone accelerometers. Features are extracted from the raw acceleration data collected by smartphones, then an unsupervised classification method called MCODE is used for activity recognition. We evaluate the effectiveness of our method on three real-world datasets, i.e., a public dataset of daily living activities and two datasets of sports activities of race walking and basketball playing collected by ourselves, and we find our method outperforms other existing methods. The results show that our method is viable to recognize physical activities using smartphone accelerometers.
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