Advanced Fall Analysis for Elderly Monitoring Using Feature Fusion and CNN-LSTM: A Multi-Camera Approach

Volume 9, Issue 6, Page No 12-20, 2024

Author’s Name: Win Pa Pa San ¹, Myo Khaing ²

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¹ Image and Signal Processing Lab, University of Computer Studies, Mandalay, Mandalay, 05071, Myanmar
² Faculty of Computer Science, University of Computer Studies, Mandalay, Mandalay, 05071, Myanmar

^a)whom correspondence should be addressed. E-mail: winpapasan@ucsm.edu.mm

Adv. Sci. Technol. Eng. Syst. J. 9(6), 12-20 (2024); DOI: 10.25046/aj090602

Keywords: Feature Fusion, Human Silhouette Image (HSI), Silhouette History Images (SHI), Dense Optical Flow (DOF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)

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Abstract

The integration of machine learning techniques in industrial production has the potential to revolutionize traditional manufacturing processes. In this study, we examine the efficacy of gradient-boosting machine learning models, specifically focusing on feature engineering techniques, applied to a novel dataset with 3D product models pertaining to work moan-hours in metal sheet stamping projects, framed as a regression task. The results indicate that LightGBM and XGBoost surpass other models, and their effectiveness is further enhanced by employing feature selection and synthetic data generation methods. The optimized LightGBM model exhibited superior performance, achieving a MAPE score of 10.78%, which highlights the effectiveness of gradient boosting mechanisms in handling heterogeneous data sets typical in custom manufacturing. Additionally, we introduce a methodology that enables domain experts to observe and critique the results through explainable AI visualizations.

Received: 15 September 2024 Revised: 30 September 2024 Accepted: 15 October 2024 Online: 3o November 2024

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Advanced Fall Analysis for Elderly Monitoring Using Feature Fusion and CNN-LSTM: A Multi-Camera Approach