Parameter-Tuned Deep Learning-Enabled Activity Recognition for Disabled People

Abstract

Elderly or disabled people can be supported by a human activity recognition (HAR) system that monitors their activity intervenes and patterns in case of changes in their behaviors or critical events have occurred. An automated HAR could assist these persons to have a more independent life. Providing appropriate and accurate data regarding the activity is the most crucial computation task in the activity recognition system. With the fast development of neural networks, computing, and machine learning algorithms, HAR system based on wearable sensors has gained popularity in several areas, such as medical services, smart homes, improving human communication with computers, security systems, healthcare for the elderly, mechanization in industry, robot monitoring system, monitoring athlete training, and rehabilitation systems. In this view, this study develops an improved pelican optimization with deep transfer learning enabled HAR (IPODTL-HAR) system for disabled persons. The major goal of the IPODTL-HAR method was recognizing the human activities for disabled person and improve the quality of living. The presented IPODTL-HAR model follows data pre-processing for improvising the quality of the data. Besides, EfficientNet model is applied to derive a useful set of feature vectors and the hyperparameters are adjusted by the use of Nadam optimizer. Finally, the IPO with deep belief network (DBN) model is utilized for the recognition and classification of human activities. The utilization of Nadam optimizer and IPO algorithm helps in effectually tuning the hyperparameters related to the EfficientNet and DBN models respectively. The experimental validation of the IPODTL-HAR method is tested using benchmark dataset. Extensive comparison study highlighted the betterment of the IPODTL-HAR model over recent state of art HAR approaches interms of different measures.