Abstract

Health is a significant factor in life. Still, people failed to get the right health care services. It is caused by restrictions on the technology used in hospitals and on access to hospitals. The Internet of Things (IoT) is a hot topic nowadays, offering many solutions across various sectors, for example, in healthcare. IoT is applied in several health interventions, including identifying illness as a protection, treating illness as a healing process, and monitoring illness as a healing solution itself. The wearable sensors utilised for monitoring blood pressure and heart rate are proven to distinguish in the initial stage. At present, deep learning (DL) techniques in this domain are promising methods for improving patient care. In this manuscript, we propose an effective Patient Monitoring with Wearable Devices Using Deep Sparse Autoencoder and Optimisation Algorithm (PMWD-DSAEOA) model in smart healthcare. The aim is to develop an effective framework for real-time health monitoring utilising wearable devices to enable proactive, personalised healthcare solutions. To accomplish that, the PMWD-DSAEOA model includes a data pre-processing step that normalises the input data using Z-scores for effective analysis. Next, the feature selection step is used, a critical stage that reduces data dimensionality and improves efficiency by implementing the northern goshawk optimisation (NGO) method. Additionally, the classification process primarily uses the stacked sparse autoencoder (SSAE) method. To further improve the model's performance, the dung beetle optimisation (DBO) method is employed for parameter tuning. To demonstrate the improved performance of the PMWD-DSAEOA approach, a comprehensive experimental study is conducted. The comparative outcomes indicated the improved features of the PMWD-DSAEOA approach.

Authors

A.N. Swamynathan¹, S. Thirumal²
Rajeswari Vedachalam Government Arts College, India¹, Arignar Anna Government Arts College, Cheyyar, India²

Keywords

Patient Monitoring, Internet of Things, Wearable Devices, Smart Healthcare, Dung Beetle Algorithm, Deep Learning, Deep Sparse Autoencoder