Abstract
Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.
Highlights
The importance of regular physiological metric monitoring and smart data analytics in the early detection and prevention of cardiovascular diseases has been reported [1,2]
The results showed a high correlation coefficient (r = 0.86 for systolic blood pressure (SBP), 0.84 for diastolic blood pressure (DBP)), which fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) standards for blood pressure (BP) monitor certification [54,55]
A flexible, wireless, and wearable biosensor patch with skin-conformal attachment and multiple biosensor integration for monitoring different vital signs is introduced in this study
Summary
The importance of regular physiological metric monitoring and smart data analytics in the early detection and prevention of cardiovascular diseases has been reported [1,2]. Monitoring different physiological signals requires multiple biosensors and devices, which increases the cost of equipment and causes inconvenience and discomfort to users [3,8]. Yung et al introduced a BP estimation method using a bidirectional layer of long short-term memory (LSTM) They extracted features from ECG and PPG signals from the dataset of Physionet’s Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) to predict systolic blood pressure (SBP) and diastolic blood pressure (DBP). In the present research, a BP estimation model based on artificial neural networks and one-channel ECG signals for unobtrusive and continuous BP monitoring is proposed. The main objective of this paper is to propose a wearable, wireless, and integrated biosensor device with an IoT-connected healthcare platform for continuous and simultaneous vital signs monitoring.
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