Pain track analysis during gestation using machine learning techniques
- Title
- Pain track analysis during gestation using machine learning techniques
- Creator
- Naik P.; Reddy V.; Shettigar R.
- Description
- During the gestation period women experience Braxton Hicks which is called the false labor, contractions during the second trimester. These contractions are not in regular intervals and also they are often unnoticed. The real labour or the true labour contractions develop late in the third trimester of the gestation usually beyond 36th week (excluding pre-term birth). Some women often fail to identify these pains in the third trimester of the gestation where an efficient facial recognition algorithm along with the support vector machine (SVM) helps them to identify these pains and take optimum care of themselves. The authors in this paper convey a mechanism to identify the pains effectively by creating a database of images pertaining to the pregnant women, her emotional states throughout the pregnancy. Using MATLAB the algorithm of decision tree is implemented and the values obtained from them help us analyze the pain type efficiently. 2021 Institute of Advanced Engineering and Science. All rights reserved.
- Source
- International Journal of Electrical and Computer Engineering, Vol-11, No. 3, pp. 2128-2133.
- Date
- 2021-01-01
- Publisher
- Institute of Advanced Engineering and Science
- Subject
- Braxton hicks; Facial recognition; Labor; Support vector machine
- Coverage
- Naik P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Reddy V., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Shettigar R., Department of Computer Science and Engineering, NMAM Institute of Technology, Nitte, India
- Rights
- All Open Access; Gold Open Access; Green Open Access
- Relation
- ISSN: 20888708
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Naik P.; Reddy V.; Shettigar R., “Pain track analysis during gestation using machine learning techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/15845.