Empowering aged persons care services: Leveraging data-driven BiLSTM-GRU integration with wearable devices

Abstract

Wearable medical devices (WMDs) have emerged as a promising medical technology to enhance patient care from a variety of medical angles. However, due to inaccurate signal measurement in sensors, the conventional model-driven framework has problems with its limited reconstruction ratio and compression ratio. A growing need for effective and individualized aged care services is created by the world's population's increasing aging. To increase the system quality and overall performance, the collected data is preprocessed to normalized data by using Z score Normalization. This study proposes a novel approach that integrates wearable device data with Elderly people's health and wellbeing may be predicted and monitored using Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) models. This data-driven strategy enables caregivers to deliver tailored care plans, timely treatments and early identification of potential health problems, which improve the aged population's overall quality of life. The BiLSTM-GRU evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (96%), Precision (93%), F1-score (95%), Sensitivity (94%), Specificity (90%) and MSE. By advancing data-driven techniques in aged care services, this research intends to pave the way for a more effective and long-lasting healthcare system that is designed to meet the needs of the senior population.