scholarly journals Psychological Stress Detection and Early Warning System Based on Wireless Network Transmission

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yaling Li
Keyword(s):  
Real Time ◽  
Wireless Network ◽  
Psychological Stress ◽  
Early Warning ◽  
Warning System ◽  
Pulse Signal ◽  
Inverse Transformation ◽  
Stress Detection ◽  
Reconstructed Signal ◽  
Network Transmission

To improve the accuracy of stress detection and stress warning errors, this paper designed a new psychological stress detection and warning system based on wireless network transmission. To achieve this objective, we established a real-time setup. The hardware of the system is composed of pulse acquisition module, signal low-pass filtering and amplification module, real-time clock module, wireless network transmission module, and power supply module, respectively. Based on the aforementioned hardware platform, the system software is designed, mainly through the construction of pulse signal noise and the use of wavelet denoising threshold for sensing wavelet packet inverse transformation, to get the reconstructed signal. The peak point of the reconstructed signal is determined, and the value of pulse signal is extracted. According to the characteristic value’s extraction results, the degree of psychological stress is quantified using the psychological stress index (PSI). When the PSI exceeds a predefined threshold, it indicates an early warning of psychological stress. The experimental results show that the psychological stress detection of our system is consistent with the expert evaluation results, the warning time is short, and the practical application effect is good.

Safety Monitoring and Warning System for Subway Construction Workers using Wearable Technology

2019 ◽  
Vol 13 ◽  
Author(s):  
Jun-hua Chen ◽  
Da-hu Wang ◽  
Cun-yuan Sun
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Early Warning ◽  
Warning System ◽  
Safety Monitoring ◽  
Wearable Technology ◽  
Construction Workers ◽  
Great Success ◽  
Subway Construction ◽  
Monitoring And Early Warning

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

A Reliability-Based Early Warning System for Real-Time Monitoring on RC Buildings

2012 ◽  
Vol 446-449 ◽  
pp. 3422-3427
Author(s):  
Wang Sheng Liu ◽  
Ming Zhao
Keyword(s):  
Probability Distribution ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Limit State ◽  
Warning System ◽  
State Function ◽  
Real Time Monitoring ◽  
Rc Buildings ◽  
Load Effect

Today there is an urgent need for effective monitoring whether for old buildings or new ones. While conventional early warning system for real-time monitoring is based on safety factor, this paper proposes a new reliability-based framework to monitor the safety of RC buildings probabilistically. The framework includes modeling resistance, predicting probability distribution of load effect, calculating reliability and setting reliability index threshold. The in-situ test data enables to update the resistance model through a Bayesian process. Meanwhile, the observed monitoring data predicts the probability distribution of load effect. FORM is used to calculate the reliability because the limit state function for real-time monitoring is linear and simple. This study shows that the reliability-based early warning system is of more scientific sense in quantifying the safety and may be applied to many engineering fields.

scholarly journals Design of Early Warning System Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 66
Author(s):  
Gan Bo ◽  
Jin Shan
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Monitoring System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Sensor Data ◽  
Landslide Monitoring ◽  
Remote Transmission ◽  
Sensor Data Acquisition

In order to solve the shortcomings of the landslide monitoring technology method, a set of landslides monitoring and early warning system is designed. It can achieve real-time sensor data acquisition, remote transmission and query display. In addition, aiming at the harsh environment of landslide monitoring and the performance requirements of the monitoring system, an improved minimum hop routing protocol is proposed. It can reduce network energy consumption, enhance network robustness, and improve node layout and networking flexibility. In order to realize the remote transmission of data, GPRS wireless communication is used to transmit monitoring data. Combined with remote monitoring center, real-time data display, query, preservation and landslide warning and prediction are realized. The results show that the sensor data acquisition system is accurate, the system is stable, and the node network is flexible. Therefore, the monitoring system has a good use value.

scholarly journals A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Liu ◽  
Yunfeng Ji ◽  
Yun Gao ◽  
Zhenyu Ping ◽  
Liang Kuang ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Traffic Accidents ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Warning System ◽  
Support Vector ◽  
Eyelid Closure ◽  
Recognition Result ◽  
Fatigue Driving

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

Review of Tools for the Prediction of Patient Deterioration in the Digital Hospital Setting. (Preprint)

2021 ◽  
Author(s):  
Kay Debby Mann ◽  
Norm Good ◽  
Farhad Fatehi ◽  
Sankalp Khanna ◽  
Victoria Campbell ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Hospital Setting ◽  
Warning System ◽  
Scoring Systems ◽  
Time Data ◽  
Time Dynamic ◽  
Patient Deterioration ◽  
Dynamic Risk ◽  
Risk Estimates

BACKGROUND Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data, and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates. OBJECTIVE This review describes published studies on the development, validation and implementation of tools for prediction of patient deterioration in hospital general wards. METHODS An electronic database search of peer-reviewed journal papers 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration - defined by unplanned transfer to intensive care unit (ICU), cardiac arrest, or death. Studies conducted solely in ICUs, emergency departments or on single diagnosis patient groups were excluded. RESULTS Forty-five publications, eligible for inclusion, were heterogeneous in design, setting and outcome measures. Most papers were retrospective studies utilizing cohort data to develop, validate or statistically evaluate prediction tools. Tools consisted of early warning, screening or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time, deal with complexities of longitudinal data and warn of deterioration risk earlier. Only a few studies detailed the results of implementation of the deterioration warning tools. CONCLUSIONS Despite relative progress on the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvement of patient outcomes. Further work is needed to realise the potential of automated predictions and updating dynamic risk estimates as part of an operational early warning system for inpatient deterioration.

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