第12次 아시아ㆍ太平洋 矯政局長會議 參加記

Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Among others, mainly radio frequency identification (RFID), wireless sensor network (WSN), and smart mobile technologies are leading this evolutionary trend. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients, personnel, and biomedical devices within hospitals and nursing institutes. Staying true to the IoT vision, we propose a smart hospital system (SHS), which relies on different, yet complementary, technologies, specifically RFID, WSN, and smart mobile, interoperating with each other through a Constrained Application Protocol (CoAP)/IPv6 over low-power wireless personal area network (6LoWPAN)/representational state transfer (REST) network infrastructure. The SHS is able to collect, in real time, both environmental conditions and patients’ physiological parameters via an ultra-low-power hybrid sensing network (HSN) composed of 6LoWPAN nodes integrating UHF RFID functionalities. Sensed data are delivered to a control center where an advanced monitoring application (MA) makes them easily accessible by both local and remote users via a REST web service. The simple proof of concept implemented to validate the proposed SHS has highlighted a number of key capabilities and aspects of novelty, which represent a significant step forward compared to the actual state of the art.

An IoT-Aware Architecture for Smart Healthcare Systems

In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.

/pdf/detecting-driver-drowsiness-based-on-sensors-a-review-54cryhhdqd.pdf

Detecting driver drowsiness based on sensors: a review.

Driver fatigue has been attributed to traffic accidents; therefore, fatigue-related traffic accidents have a higher fatality rate and cause more damage to the surroundings compared with accidents where the drivers are alert. Recently, many automobile companies have installed driver assistance technologies in vehicles for driver assistance. Third party companies are also manufacturing fatigue detection devices; however, much research is still required for improvement. In the field of driver fatigue detection, continuous research is being performed and several articles propose promising results in constrained environments, still much progress is required. This paper presents state-of-the-art review of recent advancement in the field of driver fatigue detection. Methods are categorized into five groups, i.e., subjective reporting, driver biological features, driver physical features, vehicular features while driving, and hybrid features depending on the features used for driver fatigue detection. Various approaches have been compared for fatigue detection, and areas open for improvements are deduced.

Driver Fatigue Detection Systems: A Review

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

This paper presents a prototype machine-to-machine (M2M) healthcare solution that combines mobile and IPv6 techniques in a wireless sensor network to monitor the health condition of patients and provide a wide range of effective, comprehensive, and convenient healthcare services. A low-power embedded wearable sensor measures the health parameters dynamically, and is connected, according to the concept of IPv6 over low-power wireless personal area network, to the M2M node for wireless transmission through the internet or external IP-enabled networks via the M2M gateway. A visualization module of the server program graphically displays the recorded biomedical signals on Android mobile devices used by patients and doctors at the end of the networks in real-time. Our approach for a global M2M healthcare solution is managed to process the large amount of biomedical signals through the extended network combining IPv6 technique and mobile technology for daily lifestyle to users appropriately.

Wireless Machine-to-Machine Healthcare Solution Using Android Mobile Devices in Global Networks

Driver's drowsiness and fatigue have been major causes of the serious traffic accidents, which make this an area of great socioeconomic concern. This paper describes the design of ECG (Electrocardiogram) sensor with conductive fabric electrodes and PPG (Photoplethysmogram) sensor to obtain physiological signals for car driver's health condition monitoring. ECG and PPG signals are transmitted to base station connected to the server PC via personal area network for practical test. Intelligent health condition monitoring system is designed at the server to analyze the PPG and ECG signals. Our purpose for intelligent health condition monitoring system is managed to process HRV signals analysis derived from the physiological signals in time and frequency domain and to evaluate the driver's drowsiness status.

Real time car driver's condition monitoring system

This study presents a novel approach to detect driver's drowsiness by applying two distinct methods in computer vision and image processing. The objective of this study is to combine both methods under one single profile instead of relied solely on a detection method to enhance the driver's drowsiness detection resolution. Therefore a non-intrusive drowsy-monitoring system is developed to alert the driver if driver falls into low arousal state. In physiological part, photoplethysmography (PPG) is analysed for its changes in signals waveform from awake to drowsy state. Meanwhile, eyes pattern or motion in image processing is addressed to detect driver fatigue. Genetic algorithm with template-matching approach is designed to detect eye region and estimate the drowsiness in different metric standard based on eyes behaviour. Moreover, PPG drowsy signals are integrated with eyes motion to derive the final probability model for delivering valid and reliable drowsiness detection system. Indeed, the proposed system provides high competitive edge over existing arbitrary drowsiness detection system where the driver's health and mental states can be monitored in real-time without constraints.

Real-time physiological and vision monitoring of vehicle driver for non-intrusive drowsiness detection

The proposed electrocardiogram (ECG) measurement system uses a nonintrusive ECG sensor with electrically active electrodes to measure an ECG signal without any noise effects. In this manner, the optimal position and size of the active electrodes attached to a car seat can be selected from several practical scenarios considering the physical position of the subject's heart to minimize the noise effects for the nonintrusive ECG measurement system in the car. The ECG signals from a clothed and seated occupant of the automobile are measured at a sampling rate of 100 Hz. A signal conditioning circuit was also designed to reduce the noise effects generated by the internal circuitry. The ECG signals are measured and transmitted wirelessly to a base station connected to a server PC in a personal area network for signal processing. The driver's condition is monitored wirelessly and analyzed by performing a heart rate variability analysis in the time and frequency domains.

Sang-Joong Jung

Papers

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

Wireless Machine-to-Machine Healthcare Solution Using Android Mobile Devices in Global Networks

Real time car driver's condition monitoring system

Real-time physiological and vision monitoring of vehicle driver for non-intrusive drowsiness detection

Highly sensitive driver health condition monitoring system using nonintrusive active electrodes