A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks

Akash, N. S. Prathap , N. Jahnavi , N. S. S. Revanya , U.

doi:https://doi.org/10.55041/ijsmt.v2i2.122

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A HYBRID POSTURE DETECTION FRAMEWORK: INTEGRATING MACHINE LEARNING AND DEEP NEURAL NETWORKS

AUTHORS:

N. S. Prathap , N. Jahnavi , N. S. S. Revanya , U. Akash

Mentor

B. Narasimha Rao

Affiliation

Dept. Of Computer Science and Engineering , Bonam Venkata Chalamayya College

DOI: 10.55041/ijsmt.v2i2.122

CC BY 4.0 License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

In recent years, posture detection has become increasingly important in various fields such as healthcare, ergonomics, and human-computer interaction. This project proposes a Hybrid Posture Detection Framework that leverages the strengths of both traditional Machine Learning (ML) techniques and Deep Neural Networks (DNNs) to achieve high accuracy and robustness in identifying and classifying human postures. The framework utilizes pre-processed image or sensor data to extract relevant features, which are then analyzed through a dual-layered architecture. The first layer employs ML algorithms for preliminary posture classification based on engineered features, while the second layer refines predictions using convolutional neural networks (CNNs) to learn spatial hierarchies and complex patterns.Keywords Posture Discovery, Machine literacy, Deep Learning, Hybrid Approach, SVM, Logistic Regression, Random Forest, Naive Bayes, LSTM, 1D- CNN, 2D- CNN.

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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.

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