Septic Shock Prediction by Real Time Monitoring of Heart Rate Variability

Author(s):  
Y. Yokota ◽  
Y. Kawamura ◽  
N. Matsumaru ◽  
K. Shirai
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 74721-74733 ◽  
Author(s):  
Vladimir L. Petrovic ◽  
Milica M. Jankovic ◽  
Anita V. Lupsic ◽  
Veljko R. Mihajlovic ◽  
Jelena S. Popovic-Bozovic

2010 ◽  
Vol 25 (2) ◽  
pp. 313-316 ◽  
Author(s):  
Shunji Kasaoka ◽  
Takashi Nakahara ◽  
Yoshikatsu Kawamura ◽  
Ryosuke Tsuruta ◽  
Tsuyoshi Maekawa

2000 ◽  
Author(s):  
K. Zaglaniczny ◽  
W. Shoemaker ◽  
D. S. Gorguze ◽  
C. Woo ◽  
J. Colombo

Author(s):  
Yourui Tong ◽  
Bochen Jia ◽  
Yi Wang ◽  
Si Yang

To help automated vehicles learn surrounding environments via V2X communications, it is important to detect and transfer pedestrian situation awareness to the related vehicles. Based on the characteristics of pedestrians, a real-time algorithm was developed to detect pedestrian situation awareness. In the study, the heart rate variability (HRV) and phone position were used to understand the mental state and distractions of pedestrians. The HRV analysis was used to detect the fatigue and alert state of the pedestrian, and the phone position was used to define the phone distractions of the pedestrian. A Support Vector Machine algorithm was used to classify the pedestrian’s mental state. The results indicated a good performance with 86% prediction accuracy. The developed algorithm shows high applicability to detect the pedestrian’s situation awareness in real-time, which would further extend our understanding on V2X employment and automated vehicle design.


2020 ◽  
Vol 15 ◽  
pp. 155892502097726
Author(s):  
Wei Wang ◽  
Zhiqiang Pang ◽  
Ling Peng ◽  
Fei Hu

Performing real-time monitoring for human vital signs during sleep at home is of vital importance to achieve timely detection and rescue. However, the existing smart equipment for monitoring human vital signs suffers the drawbacks of high complexity, high cost, and intrusiveness, or low accuracy. Thus, it is of great need to develop a simplified, nonintrusive, comfortable and low cost real-time monitoring system during sleep. In this study, a novel intelligent pillow was developed based on a low-cost piezoelectric ceramic sensor. It was manufactured by locating a smart system (consisting of a sensing unit i.e. a piezoelectric ceramic sensor, a data processing unit and a GPRS communication module) in the cavity of the pillow made of shape memory foam. The sampling frequency of the intelligent pillow was set at 1000 Hz to capture the signals more accurately, and vital signs including heart rate, respiratory rate and body movement were derived through series of well established algorithms, which were sent to the user’s app. Validation experimental results demonstrate that high heart-rate detection accuracy (i.e. 99.18%) was achieved in using the intelligent pillow. Besides, human tests were conducted by detecting vital signs of six elder participants at their home, and results showed that the detected vital signs may well predicate their health conditions. In addition, no contact discomfort was reported by the participants. With further studies in terms of validity of the intelligent pillow and large-scale human trials, the proposed intelligent pillow was expected to play an important role in daily sleep monitoring.


Sign in / Sign up

Export Citation Format

Share Document