Survey on Safety Devices Using IoT

Traffic congestion is becoming a huge problem, which is arising due to vehicle failure or accidents. Transportation and use of advanced technology has great importance in society and that has made many of our lives much easier. By automatic accident detection and alerting GSM & GPS based technology can be used to overcome these problems. Where as in case of Child and Women there are very few efficient security and safety measures adopted. Now in India the safety for women has become a major issue while travelling. Nowadays women think twice before taking any steps out of their homes, especially in the night time. Hence, this is unfortunately, the sad reality of our country and also due to various crimes like child abuse, rape, dowry deaths, trafficking and many more. At the time of women facing unsecured situations, there is a need to ensure safety while travelling. Hence automatic detection system needs to be established where one can send alert message to the police station or the relatives which detects the current location of the required ones by use of such technologies the women and children can get protection. Mainly in remote areas children use bicycles as means of transport from several years and nowadays, despite due to the large vailability of new and faster means, the bicycle users is not decreased. Despites the cyclists find difficult to travel within them and other vehicles find difficult to find them during night time. In case of any emergency situation faced at unknown remote areas the cyclist can send their location to required ones to help them. In this paper, report the survey on the existing mechanism for detecting locations, and sending signals and to collect parameters such as temperature of the human body, heart beat etc. using sensors. With the help of GPS and GSM we can track the location of the child, women or vehicle. Hence, by these we can save the life of person’s being injured in various locations by sending a text message using IOT technologies

2021 ◽  
Vol 1754 (1) ◽  
pp. 012233
Author(s):  
Han Hou ◽  
Guohua Cao ◽  
Hongchang Ding ◽  
Changfu Zhao ◽  
Aijia Wang

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
C.W Liu ◽  
R.H Pan ◽  
Y.L Hu

Abstract Background Left ventricular hypertrophy (LVH) is associated with increased risks of cardiovascular diseases. Electrocardiography (ECG) is generally used to screen LVH in general population and electrocardiographic LVH is further confirmed by transthoracic echocardiography (Echo). Purpose We aimed to establish an ECG LVH detection system that was validated by echo LVH. Methods We collected the data of ECGs and Echo from the previous database. The voltage of R- and S-amplitude in each ECG lead were measured twice by a study assistance blinded to the study design, (artificially measured). Another knowledge engineer analyzed row signals of ECG (the algorithm). We firstly check the correlation of R- and S-amplitude between the artificially measured and the algorythm. ECG LVH is defined by the voltage criteria and Echo LVH is defined by LV mass index >115 g/m2 in men and >95 g/m2 in women. Then we use decision tree, k-means, and back propagation neural network (BPNN) with or without heart beat segmentation to establish a rapid and accurate LVH detection system. The ratio of training set to test set was 7:3. Results The study consisted of a sample size of 953 individuals (90% male) with 173 Echo LVH. The R- and S-amplitude were highly correlated between artificially measured and the algorithm R- and S-amplitude regarding that the Pearson correlation coefficient were >0.9 in each lead (the highest r of 0.997 in RV5 and the lowest r of 0.904 in aVR). Without heart beat segmentation, the accuracy of decision tree, k-means, and BPNN to predict echo LVH were 0.74, 0.73 and 0.51, respectively. With heart beat segmentation, the signal of Echo LVH expanded to 1466, and the accuracy to predict ECG LVH were obviously improved (0.92 for decision tree, 0.96 for k-means, and 0.59 for BPNN). Conclusions Our study showed that machine-learning model by BPNN had the highest accuracy than decision trees and k-means based on ECG R- and S-amplitude signal analyses. Figure 1. Three layers of the decision tree Funding Acknowledgement Type of funding source: None


Author(s):  
Maycon L. M. Peixoto ◽  
Edson M. Cruz ◽  
Adriano H. O. Maia ◽  
Mariese C. A. Santos ◽  
Wellington V. Lobato ◽  
...  

Author(s):  
Jiangbo Wei ◽  
Chenghao Zhang ◽  
Jiaji Ma ◽  
Zhihang Li ◽  
Maliang Liu

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