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Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

文献类型：期刊论文

作者Chen, Jingcheng2,3; Sun, Yining2,3; Sun, Shaoming1,2,3
刊名SENSORS
出版日期2021-02-01
卷号21
关键词feature selection human activity recognition activity of daily living sensor fusion wearable sensors genetic algorithm coordinate calibration
DOI10.3390/s21030692
通讯作者Sun, Shaoming(smsun@iim.ac.cn)
英文摘要Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.
资助项目Anhui Provincial Key Research and Development Plan[202004a07020037] ; National Key R&D Program of China[2018YFC2001304]
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
出版者MDPI
WOS记录号WOS:000615513500001
资助机构Anhui Provincial Key Research and Development Plan ; National Key R&D Program of China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/120239]  
专题中国科学院合肥物质科学研究院
通讯作者Sun, Shaoming
作者单位1.Chinese Acad Sci Hefei, Inst Technol Innovat, Hefei 230088, Peoples R China
2.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
3.Univ Sci & Technol China, Hefei 230026, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Jingcheng,Sun, Yining,Sun, Shaoming. Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering[J]. SENSORS,2021,21.
APA Chen, Jingcheng,Sun, Yining,&Sun, Shaoming.(2021).Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering.SENSORS,21.
MLA Chen, Jingcheng,et al."Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering".SENSORS 21(2021).

入库方式： OAI收割

来源：合肥物质科学研究院

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