@article{(Open Science Index):https://publications.waset.org/pdf/10012322,
	  title     = {Smartphone-Based Human Activity Recognition by Machine Learning Methods},
	  author    = {Yanting Cao and  Kazumitsu Nawata},
	  country	= {},
	  institution	= {},
	  abstract     = {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. },
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {15},
	  number    = {11},
	  year      = {2021},
	  pages     = {394 - 397},
	  ee        = {https://publications.waset.org/pdf/10012322},
	  url   	= {https://publications.waset.org/vol/179},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 179, 2021},
	}