Version 1
: Received: 16 October 2020 / Approved: 19 October 2020 / Online: 19 October 2020 (10:44:36 CEST)
How to cite:
Khatiwada, P.; Subedi, M.; Chatterjee, A.; Wulf Gerdes, M. Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network (RNN). Preprints2020, 2020100367. https://doi.org/10.20944/preprints202010.0367.v1.
Khatiwada, P.; Subedi, M.; Chatterjee, A.; Wulf Gerdes, M. Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network (RNN). Preprints 2020, 2020100367. https://doi.org/10.20944/preprints202010.0367.v1.
Cite as:
Khatiwada, P.; Subedi, M.; Chatterjee, A.; Wulf Gerdes, M. Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network (RNN). Preprints2020, 2020100367. https://doi.org/10.20944/preprints202010.0367.v1.
Khatiwada, P.; Subedi, M.; Chatterjee, A.; Wulf Gerdes, M. Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network (RNN). Preprints 2020, 2020100367. https://doi.org/10.20944/preprints202010.0367.v1.
Abstract
— In a smart healthcare system," Human Activity Recognition (HAR)" is considered as an efficient approach in pervasive computing from activity sensor readings. The "Ambient Assisted Living (AAL)" in the home or community helps the people to provide independent care and enhanced living quality. However, many AAL models are restricted to multiple factors that include both the computational cost and system complexity. Moreover, the HAR concept has more relevance because of its applications, such as content-based video search, sports play analysis, crowd behavior prediction systems, patient monitoring systems, and surveillance systems. This paper attempts to implement the HAR system using a popular deep learning algorithm, namely "Recurrent Neural Network (RNN)" with the activity data collected from smart activity sensors over time, and it is publicly available in the "UC Irvine Machine Learning Repository (UCI)". The proposed model involves three processes: (1) data collection, (b) optimal feature learning, and (c) activity recognition. The data gathered from the benchmark repository was initially subjected to optimal feature selection that helped to select the most significant features. The proposed optimal feature selection method is based on a new meta-heuristic algorithm called "Colliding Bodies Optimization (CBO)". An objective function derived from the recognition accuracy has been used for accomplishing the optimal feature selection. The proposed model on the concerned benchmark dataset outperformed the conventional models with enhanced performance.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
