%0 Journal Article
	%A Yanting Cao and  Kazumitsu Nawata
	%D 2021
	%J International Journal of Electrical and Computer Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 179, 2021
	%T Smartphone-Based Human Activity Recognition by Machine Learning Methods
	%U https://publications.waset.org/pdf/10012322
	%V 179
	%X As smartphones are continually upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described more refined, complex and detailed. In this context, we analyzed a set of experimental data, obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model become extremely challenging. After a series of feature selection and parameters adjustments, a well-performed SVM classifier has been trained. 
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