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An Incremental Learning Method Based on Probabilistic Neural Networks and Adjustable Fuzzy Clustering for Human Activity Recognition by Using Wearable Sensors
Wang ZL(王哲龙); Jiang M(姜鸣); Hu, Yaohua; Li HY(李洪谊)
Department机器人学研究室
Source PublicationIEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
ISSN1089-7771
2012
Volume16Issue:4Pages:691-699
Indexed BySCI ; EI
EI Accession number20122815233207
WOS IDWOS:000305979500019
Contribution Rank1
Funding OrganizationThis work was supported in part by the China Post-doctoral Science Foundation under Grant 20080441102, the National Science and Technology Major Project under Grant 2010ZX04007-011-5, the China Earthquake Sector Research Funds under Grant 200808075, and the National Natural Science Foundation of China under Grant 61174027.
KeywordFuzzy Clustering Human Activity Recognition Incremental Learning Probabilistic Neural Networks Wearable Sensor
AbstractHuman activity recognition by using wearable sensors has gained tremendous interest in recent years among a range of health-related areas. To automatically recognize various human activities from wearable sensor data, many classification methods have been tried in prior studies, but most of them lack the incremental learning abilities. In this study, an incremental learning method is proposed for sensor-based human activity recognition. The proposed method is designed based on probabilistic neural networks and an adjustable fuzzy clustering algorithm. The proposed method may achieve the following features. 1) It can easily learn additional information from new training data to improve the recognition accuracy. 2) It can freely add new activities to be detected, as well as remove existing activities. 3) The updating process from new training data does not require previously used training data. An experiment was performed to collect realistic wearable sensor data from a range of activities of daily life. The experimental results showed that the proposed method achieved a good tradeoff between incremental learning ability and the recognition accuracy. The experimental results from comparison with other classification methods demonstrated the effectiveness of the proposed method further.
Language英语
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
WOS SubjectComputer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS KeywordPHYSICAL-ACTIVITY ; CLASSIFICATION ; ACCELEROMETRY
WOS Research AreaComputer Science ; Mathematical & Computational Biology ; Medical Informatics
Citation statistics
Cited Times:62[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/10022
Collection机器人学研究室
Corresponding AuthorWang ZL(王哲龙)
Affiliation1.School of Control Science and Engineering, Dalian University of Technology, Dalian 116023, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.School of Electronic Engineering, Dongguan University of Technology, Dongguan 523808, China
Recommended Citation
GB/T 7714
Wang ZL,Jiang M,Hu, Yaohua,et al. An Incremental Learning Method Based on Probabilistic Neural Networks and Adjustable Fuzzy Clustering for Human Activity Recognition by Using Wearable Sensors[J]. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE,2012,16(4):691-699.
APA Wang ZL,Jiang M,Hu, Yaohua,&Li HY.(2012).An Incremental Learning Method Based on Probabilistic Neural Networks and Adjustable Fuzzy Clustering for Human Activity Recognition by Using Wearable Sensors.IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE,16(4),691-699.
MLA Wang ZL,et al."An Incremental Learning Method Based on Probabilistic Neural Networks and Adjustable Fuzzy Clustering for Human Activity Recognition by Using Wearable Sensors".IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 16.4(2012):691-699.
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