TWFN : an architectural framework for IoMT-enabled smart healthcare system by functional heart rate variability anomaly detection based on a novel optical fiber sensor

Abstract

Human vital signs are essential to the healthcare industry and applications of Internet of medical things (IoMT). As a significant vital sign signal, heart rate variability (HRV) provides into the functioning of the human stress levels and overall well-being. However, it is inconvenient for daily long-term HRV monitoring with conventional monitoring methods under the uncomfortable feelings. Moreover, it is hard to detect anomalies in HRV data to provide real-time previews about vital signs. To address these problems, a novel fiber interferometer-based optical fiber sensor is proposed to monitor human vital signs, and we propose a novel deep learning model (TWFN). First, a novel module (ADSN) is proposed to apply graph modeling for obtaining spatial and temporal characteristics of HRV. Subsequently, an unsupervised generative adversarial network (VS-GAN) is proposed for the effective overcoming mode collapse and generator failure to converge to the target distribution better. The outcomes of the experiment might encourage the use of HRV-based healthcare application in IoMT-enabled healthcare sectors.