다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The Internet of Things (IoT) makes smart objects the ultimate building blocks in the development of cyber-physical smart pervasive frameworks. The IoT has a variety of application domains, including health care. The IoT revolution is redesigning modern health care with promising technological, economic, and social prospects. This paper surveys advances in IoT-based health care technologies and reviews the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. In addition, this paper analyzes distinct IoT security and privacy features, including security requirements, threat models, and attack taxonomies from the health care perspective. Further, this paper proposes an intelligent collaborative security model to minimize security risk; discusses how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context; addresses various IoT and eHealth policies and regulations across the world to determine how they can facilitate economies and societies in terms of sustainable development; and provides some avenues for future research on IoT-based health care based on a set of open issues and challenges.

The Internet of Things for Health Care: A Comprehensive Survey

Much has been written about theory and practice in the law, and the tension between practitioners and theorists. Judges do not cite theoretical articles often; they rarely "apply" theories to particular cases. These arguments are not revisited. Instead the Essay explores the working and interaction of theory and practice, practitioners and theorists. The Essay starts with a story about solving a legal issue using our intellectual tools - theory, practice, and their progenies: experience and "gut." Next the Essay elaborates on the nature of theory, practice, experience and "gut." The third part of the Essay discusses theories that are helpful to practitioners and those that are less helpful. The Essay concludes that practitioners theorize, and theorists practice. They use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ. Theory, practice, experience and "gut" help us think, remember, decide and create. They complement each other like the two sides of the same coin: distinct but inseparable.

Of Theory and Practice

The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.

/pdf/a-survey-on-wearable-sensor-based-systems-for-health-2d9ei1xsz2.pdf

A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis

It is demonstrated that the sensing characteristics of a semiconductor gas sensor using SnO2 can be improved by controlling fundamental factors which affect its receptor and transducer functions. The transducer function is deeply related with the microstructure of the elements, i.e., the grain size of SnO2 (D) and the depth of the surface space-charge layer (L). The sensitivity is drastically promoted when D is made comparable to or less than 2L, either by control of D for pure SnO2 elements or by control of the Debye length for impurity-doped elements. On the other hand, the receptor function is drastically modified by the introduction of foreign receptors on the surface of SnO2. In the particular cases of Pd and Ag promoters, the oxides (PdO and Ag2O) formed in air interact with the SnO2 surface to produce an electron-deficient space-charge layer, and this contributes much to promoting the gas sensitivity. For a test gas having a specific reactivity, such specificity can be utilized for exploiting gas-selective receptors, as exemplified by CuOSnO2 and La2O3SnO2 elements, which detect H2S and ethanol gas respectively very sensitively.

New approaches for improving semiconductor gas sensors

The smart shirt which measures electrocardiogram (ECG) and acceleration signals for continuous and real time health monitoring is designed and developed. The shirt mainly consists of sensors for continuous monitoring the health data and conductive fabrics to get the body signal as electrodes. The measured physiological ECG data and physical activity data are transmitted in an ad-hoc network in IEEE 802.15.4 communication standard to a base-station and server PC for remote monitoring. The wearable sensor devices are designed to fit well into shirt with small size and low power consumption to reduce the battery size. The adaptive filtering method to cancel artifact noise from conductive fabric electrodes in a shirt is also designed and tested to get clear ECG signal even though during running or physical exercise of a person.

Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring

Real time driver health condition monitoring system with drowsiness alertness was proposed. A new embedded electrocardiogram (ECG) sensor with electrically conductive fabric electrodes on the steering wheel of a car was designed to monitor the driver's health condition. The ECG signals were measured at a sampling rate of 100 Hz from the driver's palms as they stay on a pair of conductive fabric electrodes located on the steering wheel. Practical tests were conducted using an embedded ECG sensor with a wireless sensor node, and their performance was assessed under non-stop 2 h driving test. The ECG signals were measured and transmitted wirelessly to a base station connected to a server PC in personal area network environment. The driver's health condition such as the normal, fatigued and drowsy states was analysed by evaluating the heart rate variability in the time and frequency domains.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2012.0032

Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel

Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC) analysis and a support vector machine (SVM) classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO) classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use.

/pdf/detection-of-driver-drowsiness-using-wavelet-analysis-of-1xyibq48ra.pdf

Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

Driver drowsiness is among the leading causal factors in traffic accidents occurring worldwide. This paper describes a method to monitor driver safety by analyzing information related to fatigue using two distinct methods: eye movement monitoring and bio-signal processing. A monitoring system is designed in Android-based smartphone where it receives sensory data via wireless sensor network and further processes the data to indicate the current driving aptitude of the driver. It is critical that several sensors are integrated and synchronized for a more realistic evaluation of the driver behavior. The sensors applied include a video sensor to capture the driver image and a bio-signal sensor to gather the driver photoplethysmograph signal. A dynamic Bayesian network framework is used for the driver fatigue evaluation. A warning alarm is sounded if driver fatigue is believed to reach a defined threshold. The manifold testing of the system demonstrates the practical use of multiple features, particularly with discrete methods, and their fusion enables a more authentic and ample fatigue detection.

Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals

Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Many physiological signals have been proposed to detect driver drowsiness. Among these signals, an electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many EEG-based driver drowsiness detection (DDD) models gained more and more attention in recent years. However, one limitation of these studies is that these models merely estimate discrete labels and, thus, did not allow for estimating the relative severity of driver drowsiness. This paper proposes a support vector machine-based posterior probabilistic model (SVMPPM) for DDD, aimed at transforming the drowsiness level to any value of $0\sim 1$ instead of discrete labels. A fully wearable EEG system which consists of a Bluetooth-enabled EEG headband and a commercial smartwatch was used to evaluate the proposed model in a real-time way. Twenty subjects who participated in a 1-h monotonous driving simulation experiment were used to develop this model with fifteen subjects for a building model and five subjects for a testing model. According to a video-based reference, the proposed system obtained an accuracy of 91.25% for an alert group (73 out of 80 data sets), 83.78% for an early-warning group (93 out of 111 data sets), and 91.92% for a full-warning group (91 out of 99 data sets). These results indicate that the combination of the proposed SVMPPM, the EEG headband, and the wrist-worn smart device constitutes an effective, simple, and inexpensive wearable solution for DDD.

Wan-Young Chung

Papers

Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring

Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel

Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals

Smartwatch-Based Wearable EEG System for Driver Drowsiness Detection