Automated Human Activity Recognition by Colliding Bodies Optimization (CBO) -based Optimal Feature Selection with RNN
Pankaj Khatiwada, Ayan Chatterjee, Matrika Subedi · 2021
In an intelligent healthcare system, Human Activity Recognition (HAR) is considered an efficient approach in pervasive computing from activity sensor readings. The Ambient Assisted Living (AAL) in the home or community helps people provide independent care and enhanced living quality. However, many AAL models are restricted to multiple factors that include 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 study implements the HAR system using a popular deep learning algorithm, namely Recurrent Neural Network (RNN) with the activity data collected from intelligent activity sensors over time. The activity data is 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.