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...

Full description

Bibliographic Details
Main Authors: Zhiqiang Peng, Yue Zhang
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Mobile Information Systems
Online Access:http://dx.doi.org/10.1155/2018/2659142
id doaj-5d8ea6a64d0c430ca26e5711eae6b193
record_format Article
spelling 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
_version_ 1721337351695761408