A wireless sensor network compatible wearable u-healthcare monitoring system using integrated ECG, accelerometer and SpO2

Signal Quality Assessment of PPG Signals using STFT Time-Frequency Spectra and Deep Learning Approaches

Photoplethysmography (PPG) is an important signal which contains much physiological information like heart rate and cardiovascular health etc. However, PPG signals are easily corrupted by motion artifacts and body movements during their recordings, which may lead to poor quality. In order to accurately extract cardiovascular information, it is necessary to ensure high PPG quality in these applications. Although there are several existed methods to get the PPG signal quality, those algorithms are complex and the accuracies are not very high. Thus, this work proposes a deep learning network for the signal quality assessment using the STFT time-frequency spectra. A total of 5804 10s signals are preprocessed and transformed into 2D STFT spectra with 250 × 334 pixels. The STFT figures are as the input of the CNN networks, and the model gives the result as good or bad quality. The model accuracy is 98.3% with 98.9% sensitivity, 96.7% specificity, and 98.8% F1-score. And the heart rate error is much reduced after classification with the reference of ECG signals. Thus, the proposed deep learning approaches can be useful in the classification of good and bad PPG signals. As far as we know, this is the first article using deep learning methods combined with STFT time-frequency spectra to get the signal quality assessment of PPG signals.

A Secure and Lightweight Three-Factor-Based Authentication Scheme for Smart Healthcare Systems

Internet of Things (IoT) technology has recently been integrated with various healthcare devices to monitor patients' health status and share it with their healthcare practitioners. Since healthcare data often contain personal and sensitive information, healthcare systems must provide a secure user authentication scheme. Recently, Adavoudi-Jolfaei et al. and Sharma and Kalra proposed a lightweight protocol using hash function encryption only for user authentication on wireless sensor systems. In this paper, we found some weaknesses in target schemes. We propose a novel three-factor lightweight user authentication scheme that addresses these weaknesses and verifies the security of the proposed scheme using a formal verification tool called ProVerif. In addition, our proposed scheme outperforms other proposed symmetric encryption-based schemes or elliptic curve-based schemes.

Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review

Smart health-care is undergoing rapid transformation from the conventional specialist and hospital-focused style to a distributed patient-focused manner. Several technological developments have encouraged this rapid revolution of health-care vertical. Currently, 4G and other communication standards are used in health-care for smart health-care services and applications. These technologies are crucial for the evolution of future smart health-care services. With the growth in the health-care industry, several applications are expected to produce a massive amount of data in different format and size. Such immense and diverse data needs special treatment concerning the end-to-end delay, bandwidth, latency and other attributes. It is difficult for current communication technologies to fulfil the requirements of highly dynamic and time-sensitive health care applications of the future. Therefore, the 5G networks are being designed and developed to tackle the diverse communication needs of health-care applications in Internet of Things (IoT). 5G assisted smart health-care networks are an amalgamation of IoT devices that require improved network performance and enhanced cellular coverage. Current connectivity solutions for IoT face challenges, such as the support for a massive number of devices, standardisation, energy-efficiency, device density, and security. In this paper, we present a comprehensive review of 5G assisted smart health-care solutions in IoT. We present a structure for smart health-care in 5G by categorizing and classifying existing literature. We also present key requirements for successful deployment of smart health-care systems for certain scenarios in 5G. Finally, we discuss several open issues and research challenges in 5G smart health-care solutions in IoT.

Wireless transmission of biosignals for hyperbaric chamber applications

This paper presents a wireless system to send biosignals outside a hyperbaric chamber avoiding wires going through the chamber walls. Hyperbaric chambers are becoming more and more common due to new indications of hyperbaric oxygen treatments. Metallic walls physically isolate patients inside the chamber, where getting a patient’s vital signs turns into a painstaking task. The paper proposes using a ZigBee-based network to wirelessly transmit the patient's biosignals to the outside of the chamber. In particular, a wearable battery supported device has been designed, implemented and tested. Although the implementation has been conducted to transmit the electrocardiography signal, the device can be easily adapted to consider other biosignals.

An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services

As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services.

Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives.

A review on architectures and communications technologies for wearable health-monitoring systems

Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in “LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments”, published in the IEEE Transactions on Information Technology in Biomedicine, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications.