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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)
作者部门机器人学研究室
关键词Fuzzy Clustering Human Activity Recognition Incremental Learning Probabilistic Neural Networks Wearable Sensor
发表期刊IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
ISSN1089-7771
2012
卷号16期号:4页码:691-699
收录类别SCI ; EI
EI收录号20122815233207
WOS记录号WOS:000305979500019
产权排序1
资助机构This 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.
摘要Human 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.
语种英语
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
WOS类目Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
关键词[WOS]PHYSICAL-ACTIVITY ; CLASSIFICATION ; ACCELEROMETRY
WOS研究方向Computer Science ; Mathematical & Computational Biology ; Medical Informatics
引用统计
被引频次:55[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/10022
专题机器人学研究室
通讯作者Wang ZL(王哲龙)
作者单位1.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
推荐引用方式
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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