Dilemma and Solution of Traditional Feature Extraction Methods Based on Inertial Sensors
Correctly identifying human activities is very significant in modern life. Almost all feature extraction methods are based directly on acceleration and angular velocity. However, we found that some activities have no difference in acceleration and angular velocity. Therefore, we believe that for the...
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Series: | Mobile Information Systems |
Online Access: | http://dx.doi.org/10.1155/2018/2659142 |
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doaj-5d8ea6a64d0c430ca26e5711eae6b1932021-07-02T06:19:03ZengHindawi LimitedMobile Information Systems1574-017X1875-905X2018-01-01201810.1155/2018/26591422659142Dilemma and Solution of Traditional Feature Extraction Methods Based on Inertial SensorsZhiqiang Peng0Yue Zhang1The Division of Information Science and Technology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, ChinaThe Division of Information Science and Technology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, ChinaCorrectly identifying human activities is very significant in modern life. Almost all feature extraction methods are based directly on acceleration and angular velocity. However, we found that some activities have no difference in acceleration and angular velocity. Therefore, we believe that for these activities, any feature extraction method based on acceleration and angular velocity is difficult to achieve good results. After analyzing the difference of these indistinguishable movements, we propose several new features to improve accuracy of recognition. We compare the traditional features and our custom features. In addition, we examined whether the time-domain features and frequency-domain features based on acceleration and angular velocity are different. The results show that (1) our custom features significantly improve the precision of the activities that have no difference in acceleration and angular velocity; and (2) the combination of time-domain features and frequency-domain features does not significantly improve the recognition of different activities.http://dx.doi.org/10.1155/2018/2659142 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhiqiang Peng Yue Zhang |
spellingShingle |
Zhiqiang Peng Yue Zhang Dilemma and Solution of Traditional Feature Extraction Methods Based on Inertial Sensors Mobile Information Systems |
author_facet |
Zhiqiang Peng Yue Zhang |
author_sort |
Zhiqiang Peng |
title |
Dilemma and Solution of Traditional Feature Extraction Methods Based on Inertial Sensors |
title_short |
Dilemma and Solution of Traditional Feature Extraction Methods Based on Inertial Sensors |
title_full |
Dilemma and Solution of Traditional Feature Extraction Methods Based on Inertial Sensors |
title_fullStr |
Dilemma and Solution of Traditional Feature Extraction Methods Based on Inertial Sensors |
title_full_unstemmed |
Dilemma and Solution of Traditional Feature Extraction Methods Based on Inertial Sensors |
title_sort |
dilemma and solution of traditional feature extraction methods based on inertial sensors |
publisher |
Hindawi Limited |
series |
Mobile Information Systems |
issn |
1574-017X 1875-905X |
publishDate |
2018-01-01 |
description |
Correctly identifying human activities is very significant in modern life. Almost all feature extraction methods are based directly on acceleration and angular velocity. However, we found that some activities have no difference in acceleration and angular velocity. Therefore, we believe that for these activities, any feature extraction method based on acceleration and angular velocity is difficult to achieve good results. After analyzing the difference of these indistinguishable movements, we propose several new features to improve accuracy of recognition. We compare the traditional features and our custom features. In addition, we examined whether the time-domain features and frequency-domain features based on acceleration and angular velocity are different. The results show that (1) our custom features significantly improve the precision of the activities that have no difference in acceleration and angular velocity; and (2) the combination of time-domain features and frequency-domain features does not significantly improve the recognition of different activities. |
url |
http://dx.doi.org/10.1155/2018/2659142 |
work_keys_str_mv |
AT zhiqiangpeng dilemmaandsolutionoftraditionalfeatureextractionmethodsbasedoninertialsensors AT yuezhang dilemmaandsolutionoftraditionalfeatureextractionmethodsbasedoninertialsensors |
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1721337351695761408 |