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Open AccessArticle

Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

by 1,2, 1,2 and 1,2,3,*
1
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
2
University of Science and Technology of China, Hefei 230026, China
3
Chinese Academy of Sciences (Hefei) Institute of Technology Innovation, Hefei 230088, China
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(3), 692; https://doi.org/10.3390/s21030692
Received: 1 January 2021 / Revised: 16 January 2021 / Accepted: 17 January 2021 / Published: 20 January 2021
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: feature selection; human activity recognition; activity of daily living; sensor fusion; wearable sensors; genetic algorithm; coordinate calibration feature selection; human activity recognition; activity of daily living; sensor fusion; wearable sensors; genetic algorithm; coordinate calibration
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MDPI and ACS Style

Chen, J.; Sun, Y.; Sun, S. Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering. Sensors 2021, 21, 692. https://doi.org/10.3390/s21030692

AMA Style

Chen J, Sun Y, Sun S. Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering. Sensors. 2021; 21(3):692. https://doi.org/10.3390/s21030692

Chicago/Turabian Style

Chen, Jingcheng; Sun, Yining; Sun, Shaoming. 2021. "Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering" Sensors 21, no. 3: 692. https://doi.org/10.3390/s21030692

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