Research Article
Hybrid Deep-Readout Echo State Network and Support Vector Machine with Feature Selection for Human Activity Recognition
@INPROCEEDINGS{10.1007/978-3-030-72802-1_11, author={Shadi Abudalfa and Kevin Bouchard}, title={Hybrid Deep-Readout Echo State Network and Support Vector Machine with Feature Selection for Human Activity Recognition}, proceedings={Big Data Technologies and Applications. 10th EAI International Conference, BDTA 2020, and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, December 11, 2020, Proceedings}, proceedings_a={BDTA \& WICON}, year={2021}, month={7}, keywords={Hybrid technique Echo state networks Support Vector Machine Feature selection Human activity recognition Smart system}, doi={10.1007/978-3-030-72802-1_11} }- Shadi Abudalfa
Kevin Bouchard
Year: 2021
Hybrid Deep-Readout Echo State Network and Support Vector Machine with Feature Selection for Human Activity Recognition
BDTA & WICON
Springer
DOI: 10.1007/978-3-030-72802-1_11
Abstract
Developing sophisticated automated systems for assisting numerous humans such as patients and elder people is a promising future direction. Such smart systems are based on recognizing Activities of Daily Living (ADLs) for providing a suitable decision. Activity recognition systems are currently employed in developing many smart technologies (e.g., smart mobile phone) and their uses have been increased dramatically with availability of Internet of Things (IoT) technology. Numerous machine learning techniques are presented in literature for improving performance of activity recognition. Whereas, some techniques have not been sufficiently exploited with this research direction. In this paper, we shed the light on this issue by presenting a technique based on employing Echo State Network (ESN) for human activity recognition. The presented technique is based on combining ESN with Support Vector Machine (SVM) for improving performance of activity recognition. We also applied feature selection method to the collected data to decrease time complexity and increase the performance. Many experiments are conducted in this work to evaluate performance of the presented technique with human activity recognition. Experiment results have shown that the presented technique provides remarkable performance.
