Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)

<Previous Article In Volume
Next Article In Volume>

GIZMO COP- An Intelligent Security Device for Women Safety with Machine Learning

Authors
Ameelia Roseline Arulandhusamy1, *, Afreen Sadiq2, Arsha Varshinee Venugopal3, Hema Priya Hari Murugan4, Keerthana Ramasamy5
1Electronics and Communication, Engineering Science, Panimalar Engineering College, Chennai, India
2Electronics and Communication, Engineering Science, Panimalar Engineering College, Chennai, India
3Electronics and Communication, Engineering Science, Panimalar Engineering College, Chennai, India
4Electronics and Communication, Engineering Science, Panimalar Engineering College, Chennai, India
5Electronics and Communication, Engineering Science, Panimalar Engineering College, Chennai, India
*Corresponding author.
Corresponding Author
Ameelia Roseline Arulandhusamy
Available Online 17 October 2023.
DOI
10.2991/978-94-6463-250-7_14How to use a DOI?
Keywords
Women safety; GPS; GSM; Temperature; Pulse Rate; Machine Learning; Linear Regression; Arduino
Abstract

Despite the technological advancements and civilization, issues regarding personal safety especially for women and children never go out of trend. Statistics state that in India, an average of 86 rape happen daily and nearly 49 offences against women are reported for every hour. In such an unsafe society today, it is necessary to carry a smart gadget that ensures safety for women. GIZMO COP has a panic button which upon being activated sends the current location of the victim via SMS with the help of GPS and GSM to the security number fed. In the worst case, when the victim is unable to press the panic button, the temperature and pulse rate of the victim is continuously monitored by sensors and if it changes from the normal pattern or level (when feared) the location details are automatically sent. The threshold values are highly customized using linear regression from machine learning with a set of sensor values related to the user’s body condition. In addition to this, the device warns the user from entering into zones that contain hazardous gas and also has sound recording feature for legal evidence. The main advantage of this system is that it operates with cell phone towers, not requiring internet connectivity. With all the wattage values of the individual components, the average battery life is being calculated to approximately 4 hours. Regarding the threshold level for all sensor values, machine learning is implemented to detect the threshold value rather than to use an average value. By this implementation, the accuracy is enhanced by 22% to 28%. With all the basic features required for safety, this device becomes more economical and portable.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
Series
Advances in Computer Science Research
Publication Date
17 October 2023
ISBN
978-94-6463-250-7
ISSN
2352-538X
DOI
10.2991/978-94-6463-250-7_14How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ameelia Roseline Arulandhusamy
AU  - Afreen Sadiq
AU  - Arsha Varshinee Venugopal
AU  - Hema Priya Hari Murugan
AU  - Keerthana Ramasamy
PY  - 2023
DA  - 2023/10/17
TI  - GIZMO COP- An Intelligent Security Device for Women Safety with Machine Learning
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 70
EP  - 76
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_14
DO  - 10.2991/978-94-6463-250-7_14
ID  - Arulandhusamy2023
ER  -