Optimal Deep Recurrent Neural Networks for IoT-enabled Human Activity Recognition in Elderly and Disabled Persons

Abstract

Aging is related to a decrease in the ability to execute activities of day-to-day routine and decay in physical exercise, which affect mental and physical health. Elderly patients or people can depend on a human activity recognition (HAR) system, which monitors the activity interventions and patterns if any critical event or behavioral changes occur. A HAR system incorporated with the Internet of Things (IoT) environment might allow these people to live independently. While the number of groups of activities and sensor measurements is enormous, the HAR problem could not be resolved deterministically. Hence, machine learning (ML) algorithm was broadly applied for the advancement of the HAR system to find the patterns of human activity from the sensor data. Therefore, this study presents an Optimal Deep Recurrent Neural Networks for Human Activity Recognition (ODRNN-HAR) on Elderly and Disabled Persons technique in the IoT platform. The intension of the ODRNN-HAR approach lies in the recognition and classification of various kinds of human activities in the IoT environment. Primarily, the ODRNN-HAR technique enables IoT devices to collect human activity data and employs Z-score normalization as a preprocessing step. For effectual recognition of human activities, the ODRNN-HAR technique uses the DRNN model. At the final stage, the optimal hyperparameter adjustment of the DRNN model takes place using the mayfly optimization (MFO) algorithm. The result analysis of the ODRNN-HAR algorithm takes place on benchmark HAR dataset, and the outcomes are examined. The comprehensive simulation outcomes highlighted the improved recognition results of the ODRNN-HAR approach in terms of different measures.