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Missing data handling using machine learning for human activity recognition on mobile device
Prabowo O.M.a, Mutijarsa K.a, Supangkat S.H.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2016 IEEE.Human activity recognition is important technology in mobile computing era because it can be applied to many real-life, human-centric problems such as eldercare and healthcare. Successful research has so far focused on recognizing simple human activities. Currently, the smartphone is equipped with various sensors such as an accelerometer, gyroscope, digital compass, microphone, GPS and camera. The sensors have been used in various areas such as human gesture and activity recognition which is opening a new area of research and significantly impact in daily life. Activity recognition between the personal computer and smartphone is different. A mobile device has limited computational and memory capacity which has a chance that some data are missing when limitation of the mobile device is happening. In this research, some algorithms are tested to perform their ability to handling missing data, they are Bayesian Network, Multilayer Perceptron (MLP), C4.5 and k-Nearest Neighbour (k-NN). Missing data are implemented with increment scaling from 5%-40%. Optimal result based on accuracy mean is obtained by kNN with 89,4752%. Based on class, Bayesian Network obtained mean 992 recognized on Sitting class and kNN obtained mean 1010 recognized on Walking class. Multilayer Perceptron is obtained endurance point with decreasing about 9.9109% from normal experiment without missing data.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Activity recognition,Digital compass,Human activities,Human activity recognition,K nearest neighbours (k-NN),Missing data,Multi layer perceptron,Participatory Sensing[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Activity Recognition,Classification,Machine Learning,Missing Data,Mobile Computing,Participatory Sensing[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICTSS.2016.7792849[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]