Hee-Cheol Kim
Institute of Digital Anti-aging Healthcare (IDA) / Smart Wellness Lab,
Taiwan
Research Article
Human Activities Recognition Via Smartphones Using Supervised Machine Learning Classifiers
Author(s): Ahmed Younes Shdefat, Ahmed Abu Halimeh and Hee-Cheol KimAhmed Younes Shdefat, Ahmed Abu Halimeh and Hee-Cheol Kim
This paper presents a way of detecting twelve daily physical human activities such as sitting, laying, standing, attaching to table, walking, jogging, running, jumping, pushups, stairs down, going up stairs, and cycling with acceleration and gyroscope sensors data resulted from using android smart mobile phones. An android application was developed to collect raw data from the sensors. The subjects preformed the twelve activities with smart phones where it is installed. Five of the samples had been selected as train data, while the rest ten samples selected as test data. In order to classify the subjects’ raw data, a program in Matlab R2016a was developed that applies twelve supervised classification algorithms models, and then compare between them in term of accuracy and speed factors. The twelve models are divided into two categories: Six of them u.. View More»
