Daily Activities Classification on Human Motion Primitives Detection Dataset


Yap, Timothy Tzen Vun and Tong, Hau Lee and Ho, Chiung Ching and Goh, Vik Tor (2019) Daily Activities Classification on Human Motion Primitives Detection Dataset. In: Computational Science and Technology. Springer Verlag, Lecture Notes in Electrical Engineering, pp. 117-125. ISBN 978-981-13-2622-6

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The study is to classify human motion data captured by a wrist worn accelerometer. The classification is based on the various daily activities of a normal person. The dataset is obtained from Human Motion Primitives Detection [1]. There is a total of 839 trials from 14 activities performed by 16 volunteers (11 males and 5 females) ages between 19 to 91 years. A wrist worn tri-axial accelerometer was used to accrue the acceleration data of X, Y and Z axis during each trial. For feature extraction, nine statistical parameters together with the energy spectral density and the correlation between the accelerometer readings are employed to extract 63 features from the raw acceleration data. Particle Swarm Organization, Tabu Search and Ranker are applied to rank and select the positive roles for the later classification process. Classification is implemented using Support Vector Machine, k-Nearest Neighbors and Random Forest. From the experimental results, the proposed model achieved the highest correct classification rate of 91.5% from Support Vector Machine with radial basis function kernel.

Item Type: Book Section
Uncontrolled Keywords: Accelerometer
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL500-777 Aeronautics. Aeronautical engineering
Divisions: Faculty of Computing and Informatics (FCI)
Faculty of Engineering (FOE)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 26 Jan 2022 02:18
Last Modified: 26 Jan 2022 02:18
URII: http://shdl.mmu.edu.my/id/eprint/9008


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