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Интеллектуальный анализ данных
Feature generation for physical activity classification
R. V. Isachenkoab, I. N. Zharikovab, A. M. Bochkarevab, V. V. Strijovc a Moscow Institute of Physics and Technology, Russia
b Skolkovo Institute of Science and Technology, Russia
c FRC CSC RAS, Moscow Institute of Physics and Technology, Russia
Аннотация:
The paper investigates human physical activity classification problem. Time series obtained from accelerometer of a wearable device produce a dataset. Due to the high dimension of object description and low computational resources one has to state a feature generation problem. The authors propose to use the parameters of the local approximation models as informative features. The experiment is conducted on two datasets for human activity recognition using accelerometer: WISDM and USC-HAD. It compares several superpositions of various generation methods and classification models.
Ключевые слова:
wearable devices, accelerometer, time series, local approximation, classification.
Образец цитирования:
R. V. Isachenko, I. N. Zharikov, A. M. Bochkarev, V. V. Strijov, “Feature generation for physical activity classification”, Искусственный интеллект и принятие решений, 2018, no. 3, 20–27
Образцы ссылок на эту страницу:
https://www.mathnet.ru/rus/iipr212 https://www.mathnet.ru/rus/iipr/y2018/i3/p20
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Страница аннотации: | 23 | PDF полного текста: | 9 | Список литературы: | 1 |
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