Modular Technical Concepts for Ambulatory Monitoring of Risk Patients Based on Multiple Parameters and an Automatic Alarm Function

Author(s):  
Jörg Piper ◽  
Birgit Müller

Technical concepts of a multi-parameter-based system are described which can be used for continuous ambulatory monitoring of several vital signs. When critical or fatal events are detected, an automatic alarm is generated including information about the patient´s position (global positioning system, GPS) and additional messages. A lot of vital parameters are continuously monitored by “bio detectors” which are connected with a mobile data acquisition system carried by the patient. This data acquisition system interacts with a mobile phone so that an alarm can immediately be sounded in cases of critical or fatal events. Other episodes relevant for the patient´s long-term prognosis without leading to life-threatening outcomes can be stored for elective analyses without generating an alarm. Moreover, patients can manually give an alarm on demand. Potential false alarms can be manually canceled. In further stages of development these technical components could interact with electronic control systems of cars so that cars could be immediately stopped if the driver becomes unconscious.

2013 ◽  
Vol 333-335 ◽  
pp. 442-446
Author(s):  
Ru Xue ◽  
Zong Sheng Wu ◽  
Mei Yun Shao

A data acquisition system for remote vital sign is designed. The system detect humans vital signs through the body temperature, blood pressure and pulse sensors ,and transmit them to the microprocessor after processing, then the microprocessor send the data to remote monitoring center on receiving the instruction .The monitoring center analysis the data and decide what and how to do. The monitoring centers can response various change of data rapidly and implement real-time rescue guide according to different situations.


2021 ◽  
Vol 23 (1) ◽  
pp. 110-116
Author(s):  
Isaac Segovia Ramirez ◽  
Behnam Mohammadi-Ivatloo ◽  
Fausto Pedro García Márquez

Wind energy is one of the most relevant renewable energy. A proper wind turbine maintenance management is required to ensure continuous operation and optimized maintenance costs. Larger wind turbines are being installed and they require new monitoring systems to ensure optimization, reliability and availability. Advanced analytics are employed to analyze the data and reduce false alarms, avoiding unplanned downtimes and increasing costs. Supervisory control and data acquisition system determines the condition of the wind turbine providing large dataset with different signals and alarms. This paper presents a new approach combining statistical analysis and advanced algorithm for signal processing, fault detection and diagnosis. Principal component analysis and artificial neural networks are employed to evaluate the signals and detect the alarm activation pattern. The dataset has been reduced by 93% and the performance of the neural network is incremented by 1000% in comparison with the performance of original dataset without filtering process.


2013 ◽  
Vol 33 (2) ◽  
pp. 567-570
Author(s):  
Zeping YANG ◽  
Deqiang LIU ◽  
Qian WANG ◽  
Qiangming XIANG

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