Участник:Aignatov
Материал из MachineLearning.
(Новая: '''Игнатов Андрей Дмитриевич''' МФТИ, ФУПМ, 174) |
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| + | == Отчеты о научно-исследовательской работе == | ||
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| + | === Весна 2014, 4-й семестр === | ||
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| + | '''Human activity types recognition using quasiperiodic sets of time series''' | ||
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| + | This paper investigates the classification problem of time series, received from the accelerometer of mobile phone. Primarily it solves the problem of time series segmentation, assuming that each segment corresponds to one fundamental period of motion. The obtained segments refer to various types of human physical activity. One must to detect a type for each segment. The principal component analysis is applied to extract the fundamental period and to remove noise from the data. To recognize activities we use k-nearest neighbor method and neural network as alternative. We verify accuracy of obtained algorithms by testing them on the sample of measured dataset and compare proposed methods. | ||
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| + | '''Публикация''' | ||
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| + | *{{Статья | ||
| + | |автор = Ignatov A.D., Strijov V. V. | ||
| + | |название = Human activity types recognition using quasiperiodic sets of time series. | ||
| + | |журнал = Multimedia Tools and Applications | ||
| + | |год = 2014 | ||
| + | |язык = english | ||
| + | }} | ||
Версия 09:06, 23 августа 2014
Игнатов Андрей Дмитриевич
МФТИ, ФУПМ, 174
Отчеты о научно-исследовательской работе
Весна 2014, 4-й семестр
Human activity types recognition using quasiperiodic sets of time series
This paper investigates the classification problem of time series, received from the accelerometer of mobile phone. Primarily it solves the problem of time series segmentation, assuming that each segment corresponds to one fundamental period of motion. The obtained segments refer to various types of human physical activity. One must to detect a type for each segment. The principal component analysis is applied to extract the fundamental period and to remove noise from the data. To recognize activities we use k-nearest neighbor method and neural network as alternative. We verify accuracy of obtained algorithms by testing them on the sample of measured dataset and compare proposed methods.
Публикация
- Ignatov A.D., Strijov V. V. Human activity types recognition using quasiperiodic sets of time series. // Multimedia Tools and Applications. — 2014.
