Abstract
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.
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Acknowledgement
The authors would like to thank Bruno et al. from Università degli Studi, Geneva for offering the use of the database in this research. Financial support from Multimedia University under the Multimedia University Capex Fund with Project ID MMUI/CAPEX170008, & the Ministry of Higher Education, Malaysia, under the Fundamental Research Grant Scheme with grant number FRGS/1/2015/SG07/MMU/02/1, and TM R&D (UbALive) are gratefully acknowledged
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Chin, Z.H., Ng, H., Yap, T.T.V., Tong, H.L., Ho, C.C., Goh, V.T. (2019). Daily Activities Classification on Human Motion Primitives Detection Dataset. In: Alfred, R., Lim, Y., Ibrahim, A., Anthony, P. (eds) Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 481. Springer, Singapore. https://doi.org/10.1007/978-981-13-2622-6_12
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DOI: https://doi.org/10.1007/978-981-13-2622-6_12
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