Deep Transfer Learning Driven Automated Fall Detection for Quality of燣iving of Disabled Persons

Abstract

Mobile communication and the Internet of Things (IoT) technologies have recently been established to collect data from human beings and the environment. The data collected can be leveraged to provide intelligent services through different applications. It is an extreme challenge to monitor disabled people from remote locations. It is because day-to-day events like falls heavily result in accidents. For a person with disabilities, a fall event is an important cause of mortality and post-traumatic complications. Therefore, detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate. The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-Driven Automated Fall Detection (WOADTL-AFD) technique to improve the Quality of Life for persons with disabilities. The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals. To attain this, the proposed WOADTL-AFD model initially uses a modified SqueezeNet feature extractor which proficiently extracts the feature vectors. In addition, the WOADTL-AFD technique classifies the fall events using an extreme Gradient Boosting (XGBoost) classifier. In the presented WOADTL-AFD technique, the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model. The proposed WOADTL-AFD technique was experimentally validated using the benchmark datasets, and the results confirmed the superior performance of the proposed WOADTL-AFD method compared to other recent approaches.