Acceleration Feature Extraction of Human Body Based on Wearable Devices

Zhenzhen Huang, Qiang Niu, Ilsun You and Giovanni Pau
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Zhenzhen Huang: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China
Qiang Niu: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China
Ilsun You: Department of Information Security Engineering, Soonchunhyang University, Asan 31538, Korea
Giovanni Pau: Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy

Energies, 2021, vol. 14, issue 4, 1-18

Abstract: Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

Keywords: feature extraction; wearable device; acceleration sensor; behavior recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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