Real-Time Health-Monitoring Network for Disabled and Elderly People

2004 ◽  
pp. 377-384 ◽  
Author(s):  
Yung Bok Kim ◽  
Jong Yong Kim
Keyword(s):  
Real Time ◽  
Elderly People ◽  
Health Monitoring ◽  
Monitoring Network

scholarly journals A Remote Monitoring System of Logistics Carrier Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 4
Author(s):  
Wang Weidong
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Monitoring Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Remote Monitoring System ◽  
Monitoring Center

To improve the efficiency of the remote monitoring system for logistics transportation, we proposed a remote monitoring system based on wireless sensor network and GPRS communication. The system can collect information from the wireless sensor network and transmit the information to the ZigBee interpreter. The monitoring system mainly includes the following parts: Car terminal, GPRS transmission network and monitoring center. Car terminal mainly consists by the Zigbee microcontroller and peripherals, wireless sensor nodes, RFID reader, GPRS wireless communication module composed of a micro-wireless monitoring network. The information collected by the sensor communicates through the GPRS and the monitoring center on the network coordinator, sends the collected information to the monitoring center, and the monitoring center realizes the information of the logistics vehicle in real time. The system has high applicability, meets the design requirements in the real-time acquisition and information transmission of the information of the logistics transport vehicles and goods, and realizes the function of remote monitoring.

Efficient Signal Conditioning Techniques for Brain Activity in Remote Health Monitoring Network

2013 ◽  
Vol 13 (9) ◽  
pp. 3276-3283 ◽  
Author(s):  
Gundlapalli Venkata Sai Karthik ◽  
Shaik Yasmin Fathima ◽  
Muhammad Zia Ur Rahman ◽  
Shaik Rafi Ahamed ◽  
Aime Lay-Ekuakille
Keyword(s):  
Health Monitoring ◽  
Brain Activity ◽  
Monitoring Network ◽  
Signal Conditioning ◽  
Remote Health Monitoring ◽  
Remote Health

scholarly journals Arrays of Conformable Ultrasonic Lamb Wave Transducers for Structural Health Monitoring with Real-time Electronics

2014 ◽  
Vol 87 ◽  
pp. 1266-1269 ◽  
Author(s):  
L. Capineri ◽  
A. Bulletti ◽  
M. Calzolai ◽  
P. Giannelli ◽  
D. Francesconi
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Structural Health

scholarly journals Traffic sensor health monitoring using spatiotemporal graphical modeling

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Linjiang Wu ◽  
Chao Liu ◽  
Tingting Huang ◽  
Anuj Sharma ◽  
Soumik Sarkar
Keyword(s):  
Health Status ◽  
Real Time ◽  
Sensor Network ◽  
Health Monitoring ◽  
Series Data ◽  
Spatiotemporal Pattern ◽  
Graphical Modeling ◽  
Traffic Sensors ◽  
Wide Range ◽  
Sensor Health

Accurate traffic sensor data is essential for traffic operation management systems and acquisition of real-time traffic surveillance data depends heavily on the reliability of the traffic sensors (e.g., wide range detector, automatic traffic recorder). Therefore, detecting the health status of the sensors in a traffic sensor network is critical for the departments of transportation as well as other public and private entities, especially in the circumstances where real-time decision is required. With the purpose of efficiently determining the sensor health status and identifying the failed sensor(s) in a timely manner, this paper proposes a graphical modeling approach called spatiotemporal pattern network (STPN). Traffic speed and volume measurement sensors are used in this paper to formulate and analyze the proposed sensor health monitoring system and historical time-series data from a network of traffic sensors on the Interstate 35 (I-35) within the state of Iowa is used for validation. Based on the validation results, we demonstrate that the proposed approach can: (i) extract spatiotemporal dependencies among the different sensors which leads to an efficient graphical representation of the sensor network in the information space, and (ii) distinguish and quantify a sensor issue by leveraging the extracted spatiotemporal relationship of the candidate sensor(s) to the other sensors in the network.

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