Seamless N-screen Wireless MAC structure in Wireless U-health Systems

Integrated non-invasive biochemical and biophysical sensing systems for health and performance monitoring: A systems perspective

Wearable health monitoring systems (WHMSs) have become the most effective and practical solutions to provide users with low-cost, noninvasive, long-term continuous health monitoring. Authentication is one of the key means to ensure physiological information security and privacy. Although numerous authentication protocols have been proposed, few of them cater to crossdomain WHMSs. In this paper, we present an efficient and provably secure crossdomain multifactor authentication protocol for WHMSs. First, we propose a ticket-based authentication model for multidomain WHMSs. Specifically, a mobile device of one domain can request a ticket from the cloud server of another domain with which wearable devices are registered and remotely access the wearable devices with the ticket. Secondly, we propose a crossdomain three-factor authentication scheme based on the above model. Only a doctor who can present all three factors can request a legitimate ticket and use it to access the wearable devices. Finally, a comprehensive security analysis of the proposed scheme is carried out. In particular, we give a provable security analysis in the random oracle model. The comparisons of security and efficiency with the related schemes demonstrate that the proposed scheme is secure and practical.

Wearable health monitoring system based on human motion state recognition

We propose a middleware platform built on wireless USB (WUSB) over wireless body area networks (WBAN) hierarchical protocol for wearable health-monitoring systems (WHMS). The proposed middleware platform is composed of time-synchronization and localization solutions. It is executed on the basis of WUSB over WBAN protocol at each wearable sensor node comprising the WHMS. In the platform, firstly, the time-synchronization middleware is executed. After that, a WHMS host calculates the location of a receiving sensor node by using the difference between the times at which the sensor node received different WBAN beacon frames sent from the WHMS host. The WHMS host interprets the status and motion of the wearable body-sensor objects.

Chronic knee conditions and musculoskeletal injuries are prevalent and pose significant challenges to global healthcare systems. Current diagnostic methods, such as MRI and X-ray, provide valuable insights but are limited by high costs and the inability to continuously monitor knee health. In response to these challenges, the proposed work makes the following contributions. Firstly, a novel wearable knee health monitoring system is developed, incorporating a flexible composite piezoelectric film that captures subtle patellar displacements as early indicators of knee joint abnormalities. Secondly, a Bayesian machine learning framework incorporating both expert knowledge and diagnostic data is proposed to address the potential limitations of current machine learning-based diagnostic methods, such as low accuracy and poor interpretability in medical practice. This framework integrates causal discovery algorithms, clinical insights, and medical expertise to identify the key relationships between machine learning models and medical principles, ultimately improving diagnostic accuracy. Finally, the proposed system achieves an 83.3% success rate in distinguishing between healthy, sub-healthy, and unhealthy knee states, outperforming traditional methods with an accuracy improvement of 6.4% to 17%. Furthermore, the system integrates a wireless sensor network and user-friendly software, enabling convenient monitoring and diagnosis in both clinical and home settings. The main innovation of this study lies in the design of a wearable sensor network structure based on patellar activity and a medical knowledge-guided Bayesian framework to enable a three-state classification diagnosis, namely, healthy, sub-healthy, and unhealthy. This system is particularly suited for knee health assessments in outpatient clinics, community settings, and home environments..

In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the realization of the core of this system, i.e., the Cardiac Health Assessment and Monitoring Platform (CHAMP), with respect to its hardware, firmware, and wireless design features, is presented. The feature values from the CHAMP system have been correlated with myo-cardiac functions obtained from actual heart failure (HF) patients. The usability of this MCG-based cardiac health monitoring smart clothing system has also been evaluated with technology acceptance model (TAM) analysis and the results indicate that the subject shows a positive attitude toward using this wearable MCG-based cardiac health monitoring and early warning system.

Due to the capacity of processing signal with low energy consumption, compressive sensing (CS) has been widely used in wearable health monitoring system for arrhythmia classification of electrocardiogram (ECG) signals. However, most existing works focus on compressive sensing reconstruction, in other words, the ECG signals must be reconstructed before use. Hence, these methods have high computational complexity. In this paper, the authors propose a cardiac arrhythmia classification scheme that performs classification task directly in the compressed domain, skipping the reconstruction stage. The proposed scheme first employs the Pan–Tompkins algorithm to preprocess the ECG signals, including denoising and QRS detection, and then compresses the ECG signals by CS to obtain the compressive measurements. The features are extracted directly from these measurements based on principal component analysis (PCA), and are used to classify the ECG signals into different types by the proposed semi-supervised learning algorithm based on support vector machine (SVM). Extensive simulations have been performed to validate the effectiveness of the proposed scheme. Experimental results have shown that the proposed scheme achieves an average accuracy of [Formula: see text] at a sensing rate of 0.7, compared to an accuracy of [Formula: see text] for noncompressive ECG data.

Continuous monitoring of heart activity is essential for diagnosing and managing various cardiovascular conditions, especially in vulnerable populations such as children, expectant mothers, and the elderly. However, developing textile-based biopotential sensors that provide high sensitivity, broad sensing range, rapid response, and exceptional comfort remains challenging. Conventional Ag/AgCl electrodes, while effective, often cause skin irritation, allergic reactions, and have limited reusability, making them unsuitable for long-term monitoring. To address these limitations, a cost-effective knitted fabric-based non- adhesive electrode for wearable applications was developed, exhibiting low impedance (approximately 6 ohms) and an average SNR of 36.32 dB. Material characterization using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) confirmed the fabric’s resistive behavior with minimal capacitive and inductive components. Comparative studies with Ag/AgCl electrodes demonstrated a cosine similarity of 1, indicating comparable performance. The findings highlight the potential of the proposed textile based electrode for reliable, comfortable, and durable integration into wearable health monitoring systems.

We have previously developed a wearable health monitoring system embedded in a wig. In this paper we introduce our system and derive preliminary guidelines to build such wearable devices for healthcare purposes. The major goal of the system is to monitor and detect falling in addition to measuring heart rate, body temperature, and current location of a patient who is wearing the device, and send text messages to pre-configured recipients in emergency. Preliminary guidelines were developed using a focused group interview with healthcare experts, which include form factor, wearing area, motivation, target, and additional functions. We are currently in the process of developing an improved device based on the derived insights.

In this paper, a wearable health monitoring system based on wireless data transmission technology is designed for heart and blood health monitoring. In this system, new three-dimensional (3D) graphene foam sensors are used to capture heart rate signals in real time, and a microprocessor STM32 is used as the main control chip for sending the collected data to the mobile device through low-power Bluetooth. Meanwhile, the blood oxygen and blood pressure data are collected by the mobile device, and then, the acquired physical health data and location information are packaged, stored and sent to the cloud server through 4G communication circuit, so the remote monitoring of the physical health of the subject in real time by the cloud server can be realized.

Thin gold films on silicone display large reversible change in electrical resistance upon stretching. Eutectic liquid metal conductors maintain bulk metal conductivity, even upon extensive elongation. When integrated together, the soft metals enable multidirectional, large strain sensor skin. Their fabrication process combines thermal evaporation of thin gold film patterns through stencil mask with microplotting of eutectic gallium indium microwires, and packaging in silicone rubber. Using three-element rectangular rosettes, we demonstrate a sensor skin that can reliably and locally quantify the plane strain vector in surfaces subject to stretch (up to 50% strain) and indentation. This hybrid technology will find applications in soft robotics, prosthetics and wearable health monitoring systems.

Physical activity recognition using accelerometry is a rapidly advancing field with significant implications for healthcare, sports science, and wearable technology. This research presents an interesting approach for classifying physical activities using solely accelerometry data, signals that were taken from the available “MHEALTH dataset” and processed through artificial neural networks (ANNs). The methodology involves data acquisition, preprocessing, feature extraction, and the application of deep learning algorithms to accurately identify activity patterns. A major innovation in this study is the incorporation of a new feature derived from the radius of curvature. This time-domain feature is computed by segmenting accelerometry signals into windows, conducting double integration to derive positional data, and subsequently estimating a circumference based on the positional data obtained within each window. This characteristic is computed across the three movement planes, providing a robust and comprehensive feature for activity classification. The integration of the radius of curvature into the ANN models significantly enhances their accuracy, achieving over 95%. In comparison with other methodologies, our proposed approach, which utilizes a feedforward neural network (FFNN), demonstrates superior performance. This outperforms previous methods such as logistic regression, which achieved 93%, KNN models with 90%, and the InceptTime model with 88%. The findings demonstrate the potential of this model to improve the precision and reliability of physical activity recognition in wearable health monitoring systems.

The general field of health monitoring and health information exchange has garnered a keen research and market interest for the past few decades. This is indicated by the development of a plethora of wearable health monitoring systems, as well as research efforts and projects concerning the security issues in health information exchange. In this paper, a virtual doctor prototype for quick diagnosis and secure health information exchange is presented. The measured physiological data that are acquired by a wearable health monitoring system are modeled as words of a fuzzy formal language and are used by a trained and personalized neural network to diagnose the patient's health status. Moreover, we present a secure architectural scheme, which secures the transmitted data and uses the medical doctors' biometrics to authorize them and give them access to these data.

Nowadays, monitoring health systems in robust technology has been extensively applied in the sports field. Even though massive utilization of wearable device technologies aims to quantify athlete performance, inconsistent performance still exists between training sessions and competition. The rigorous discussion about the latest research in monitoring technological systems will help trainers obtain accurate data about athlete performance. This paper focuses on the athlete monitoring system in terms of psychological and physiological parameters and applications in individual sports based on Internet of Things (IoT) Technology. The study incorporates three factors: the parameters that affect athlete performance, multiple device sensors in sports health monitoring, and IoT technology’s application for athletes. Based on analysis and observation, efficient sports health monitoring can effectively enhance athlete performance in physiological and psychological conditions. An IoT system encompasses four main aspects: sensing, networking, data processing and application layer. These aspects provide real-time information on the athlete’s body condition during training and games. Therefore, this monitoring system greatly assists coaches in designing practical training and activities for athletes. It is highlighted that wearable health monitoring systems by IoT technology will be further built based on athlete requirements.

In the last few years, wearable health monitoring systems have gained the attention of various researchers in order to cope with the rising cost of the health care systems. This paper addresses the use of standard protocols, particularly IEEE 802.15.4 and ZigBee, which are capable of supporting the Quality of Service (QoS) requirements in Wireless Body Area Networks. Simulation study on the routing protocols was done to investigate the protocol suitable for Body Area Networks and AODV was found to meet the requirements of energy and QoS.