Open Access

ARTICLE

Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

by Sarah B. Basahel¹, Saleh Bajaba², Mohammad Yamin³, Sachi Nandan Mohanty⁴, E. Laxmi Lydia^5,*

1 Department of Management Information Systems, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia
2 Department of business Administration, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Department of Management Information Systems, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
4 School of Computer Science &Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India
5 Department of Computer Science and Engineering, GMR Institute of Technology, Andhra Pradesh, Rajam 532127, India

* Corresponding Author: E. Laxmi Lydia. Email:

Computers, Materials & Continua 2023, 75(1), 1353-1369. https://doi.org/10.32604/cmc.2023.036453

Received 30 September 2022; Accepted 19 November 2022; Issue published 06 February 2023

Abstract

The current advancement in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT) transformed the traditional healthcare system into smart healthcare. Healthcare services could be enhanced by incorporating key techniques like AI and IoT. The convergence of AI and IoT provides distinct opportunities in the medical field. Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population. Therefore, earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support. Lately, the emergence of IoT, AI, smartphones, wearables, and so on making it possible to design fall detection (FD) systems for smart home care. This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems (TWODL-FDSHS). The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system. Initially, the presented TWODL-FDSHS technique exploits IoT devices for the data collection process. Next, the TWODL-FDSHS technique applies the TWO with Capsule Network (CapsNet) model for feature extraction. At last, a deep random vector functional link network (DRVFLN) with an Adam optimizer is exploited for fall event detection. A wide range of simulations took place to exhibit the enhanced performance of the presented TWODL-FDSHS technique. The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30% on the URFD dataset.

---

Keywords

---