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Open AccessArticle

Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model

1
Centre for Higher Studies and Research, Bangladesh University of Professionals, Mirpur Cantonment, Dhaka-1216, Bangladesh
2
Department of Computer Science and Engineering, University of Asia Pacific, 74/A, Green Road, Dhaka-1205, Bangladesh
3
School of Computer Science and Engineering, University of Aizu, Fukushima 965-8580, Japan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 317; https://doi.org/10.3390/s20010317
Received: 31 October 2019 / Revised: 30 December 2019 / Accepted: 30 December 2019 / Published: 6 January 2020
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2019)
Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. We validated our model with a benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification. View Full-Text
Keywords: human activity recognition (HAR); feature selection; machine learning; SVM; sensor; accelerometer; gyroscope human activity recognition (HAR); feature selection; machine learning; SVM; sensor; accelerometer; gyroscope
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Ahmed, N.; Rafiq, J.I.; Islam, M.R. Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model. Sensors 2020, 20, 317.

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