Miniaturized and Wearable Electrocardiogram (ECG) Device with Wireless Transmission

ECG Holter is a device used to acquire and monitor the user heart rhythm. However, it is available only in a major healthcare facility as it is very costly. The objective of this work is to develop a portable ECG monitoring device with wireless transmission for early arrhythmia detection and personal monitoring. The heart of the device is based on Atmel ATmega328p processor, which acquires user ECG data through Analog Devices AD8232 ECG analog front-end chip. Data captured is stored offline in memory card before it is transmitted wirelessly to a cloud server for analysis purpose. Experiments indicate that the device able to sample the ECG data up to 1000 samples per second and Wi-Fi based transmission serves the best for data transfer to the cloud server. User and physician can easily access the data for viewing and analysis, eliminating the needs for users to travel to the hospital for ECG acquisition

A Review on Requirement of Wireless Sensor Network in Healthcare Applications

An assortment of uses depend on Wireless AdHoc and Sensor Networks (WASN) which has pulled in individuals from a wide number of regions demonstrating its utility extents from protection to farming, climate guaging to pre-fiasco discovery, geography to mineralogy, catastrophe alleviation frameworks to medicinal care, vehicle following to territory checking, and a considerable measure many. In the field of therapeutic sciences the uses of WASN are new however have left an incredible effect on the psyches of the two analysts and specialists. Medicinal determination and test examination like observing the patients, detecting exceptional and basic indications physically and rationally should be possible utilizing sensor systems for the therapeutic care. The potential restorative utilizations of WASN are 'Constant, nonstop patient observing', 'Home checking for interminable and elderly patients', 'Gathering of long haul databases of clinical information'. Alternate applications can be giving therapeutic supervision to individuals in remote zones and for detecting vast mischances, fires, fear based oppressor assaults and remote crucial sign checking facilitating the activity of specialists. In this paper we have attempted to make an overview of all the conceivable utilizations of WASN in the field of therapeutic Sciences

Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed

Ambient Intelligence in Healthcare: A State-of-the-Art

Information technology advancement leads to an innovative paradigm called Ambient Intelligence (AmI). A digital environment is employed along with AmI to enable individuals to be aware to their behaviors, needs, emotions and gestures. Several applications of the AmI systems in healthcare environment attract several researchers. AmI is considered one of the recent technologies that support hospitals, patients, and specialists for personal healthcare with the aid of artificial intelligence techniques and wireless sensor networks. The improvement in the wearable devices, mobile devices, embedded software and wireless technologies open the doors to advanced applications in the AmI paradigm. The WSN and the BAN collect medical data to be used for the progress of the intelligent systems adapted inevitably. The current study outlines the AmI role in healthcare concerning with its relational and technological nature. Health

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

Background and objectives - Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. Methods - A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. Results - The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. Conclusions - In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal

Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications

The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures

Application of Wireless Sensor Networks to Healthcare Promotion

Born on military applications, wireless sensor networks(WSNs) application grew on the promise of environment sensing and data processing capability at low cost. These networks can hold hundreds or even thousands of smart sensing nodes with processing and sensing capabilities and even integrated power through a dedicated battery. This paper surveys on the application of wireless sensor networks to healthcare promotion, namely with the use of biosensor technology applied to body sensor networks. On a wireless body sensor network, a person wears biosensors to gather data, while doing their daily activities. Currently, engineers and medical staff are cooperating on findingnew ways to properly gather meaningful data on-site and achieve a convenient way to process these data for research and on-site medical decision. New challenges that such approach brings are also considered. Moreover, it is shown that wireless sensor networks provide the technology to built wireless sensing and create a convenient infrastructure for multiple data gathering in healthcare applications. Together with real successful examples, we demonstrate the great usefulness of wireless sensor networks in healthcare promotion. The paper concludes with some guidelines for future work