01961nas a2200205   4500000000100000000000100001008004100002260001200043653001900055653002500074653001800099100001900117700001700136245009500153856007400248300001000322490000600332520140300338022001401741       2012                            d  c06/201210aClassification10aActivity recognition10aMobile Device1 aPekka Siirtola1 aJuha Röning00aRecognizing Human Activities User-independently on Smartphones Based on Accelerometer Data  uhttp://www.ijimai.org/journal/sites/default/files/IJIMAI20121_5_5.pdf  a38-450 v13 aReal-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject's trousers. Two classifiers were compared, knn (k nearest neighbors) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates, which are obtained by implementing models to mobile activity recognition application which currently supports two operating systems: Symbian^3 and Android. The results show that the presented method is light and, therefore, suitable for be used in real-time recognition. In addition, the recognition rates on the smartphones were encouraging, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. Also, the results show that the method presented is not an operating system dependent.
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