Software defined radio frequency sensing framework for Internet of Medical Things.

The escalating demand for biomedical systems that can precisely diagnose and manage critical diseases underscores the need for innovative solutions. A non-invasive and intelligent Internet of Medical Things (IoMT) system emerges as a promising technology, potentially enabling physicians to assess patients with reduced health risks. The respiratory rate is a pivotal vital sign among the primary clinical assessments. The allure of Radio Frequency (RF) sensing lies in its ability to monitor respiratory patterns without direct contact. However, the practical implementation of such systems often necessitates supplementary hardware to manage the extensive data and radio functionalities, leading to concerns related to cost and feasibility. Software-Defined Radio (SDR) technology presents itself as a viable solution to these challenges. This research introduces a comprehensive framework for the IoMT system, aiming to diagnose respiratory abnormalities early through RF sensing and SDR technology. We employ a deep learning framework and compare its performance with traditional machine learning models to ensure reliable and precise classification of respiratory abnormalities. The achieved results underscore the superiority of deep learning frameworks over conventional machine learning models in classifying respiratory anomalies. Specifically, the deep learning framework exhibits exceptional performance in discerning the temporal dependencies and patterns inherent in respiratory abnormalities, achieving an average accuracy exceeding 98% for each respiratory abnormality classification. [Display omitted] • Proposed innovative IoMT framework, which harnesses the power of Radio Frequency (RF) sensing and Software Defined Radio (SDR) technology to provide an early and contactless diagnosis of respiratory abnormalities, including sleep apnea. This system is designed with the patient's comfort in mind, eliminating the need for invasive sensors or tethered devices. • To Ensure the accuracy and reliability of the data, we employ advanced signal-processing algorithms. These algorithms refine the raw data, eliminating noise and artifacts, thereby enhancing the accuracy of respiratory rate measurements. • The proposed approach is the implementation of a deep learning framework, specifically designed for classifying respiratory patterns. In comparative analyses, this framework consistently outperforms conventional machine learning models, highlighting its effectiveness and potential to revolutionize respiratory health monitoring. [ABSTRACT FROM AUTHOR]