Effective integration of the internet of things and ensemble learning approaches for enhancing sudden topple recognition assisted by cloud computing technology

Abstract

Falls provide a significant public health hazard globally for the senior population. Untreated Sudden Topple in the elderly leads to functional loss and a notable decline in mobility, autonomy, and quality of life. Early identification of Sudden Topple is essential for a person's well-being or to provide needed care. Telehealth data centers need scalable processing and storing resources to accommodate the increasing number of individuals. Specialized methods that enable the transfer of just pertinent data are necessary. This study presents a Hybrid System composing Cloud Computing and the Internet of Things (IoT) (HS-CC-IoT) to monitor many elderly individuals, identify Sudden Topple, and alert caretakers. The experiments were conducted to reveal the necessary criteria for facilitating the operation of large-scale systems. The research assessed many machine learning algorithms for their appropriateness in detection. The experimental tests to identify sudden topples are in cloud-based data centers and on an Edge IoT gadget with an Ensemble Learning Algorithm. Experiments on the user-to-cloud data transfer showed that a substantial decrease in the quantity of saved and transferred data is possible when conducting Sudden Topple identification on the Edge.