Hybrid Posture Detection Framework: Connecting Deep Neural Networks and Machine Learning

DOI

10.2991/978-94-6463-471-6_139How to use a DOI?

Keywords

Artificial Intelligence; SVM; Logistic Regression; KNN; and 2D-convolutional Neural Network; 2D-CNN; ML and DL

Abstract

Many researchers in the fields of artificial intelligence and human sensing have been attempting to find a solution to the issue of posture detection. Posture recognition for the purpose of remote geriatric health monitoring, including standing, sitting, and walking. Most recent research has used conventional ML classifiers for posture recognition. When these algorithms are used for posture detection, the accuracy drops a bit. An innovative hybrid method for posture detection has been created by combining ML classifiers such as Support Vector Machine (SVM), Logistic Regression (KNN), Decision Tree, Naive Bayes, Random Forest, Linear Discrete Analysis, and Quadratic Discrete Analysis with DL classifiers such as Long Short-Term Memory (LSTM) and Bidirectional LSTM, 2D-convolutional Neural Networks (2D-CNN), and 1D-ConvolutionalNetworks (1DNN). The goal of combining DL and ML algorithms in a hybrid fashion is to boost their prediction capabilities. With our experiments on a popular benchmark dataset, we achieved an accuracy of 98% or better.

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.

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TY  - CONF
AU  - N. Jeevana Jyothi
AU  - K. Lakshmi Devi
AU  - T. Praneetha
AU  - R. Annapurna
AU  - V. Siva
AU  - S. Naveen
PY  - 2024
DA  - 2024/07/30
TI  - Hybrid Posture Detection Framework: Connecting Deep Neural Networks and Machine Learning
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1435
EP  - 1442
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_139
DO  - 10.2991/978-94-6463-471-6_139
ID  - Jyothi2024
ER  -