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Experimental Result Paper | Computer Science | India | Volume 12 Issue 11, November 2023

Effi-FallNet: Harnessing EfficientNet and LSTM for Advanced Video-Based Fall Detection

Naima Hammad ^[2] | Aryan Sawhney

Abstract: In the rapidly evolving realm of deep learning, the fusion of diverse architectures offers promising avenues for enhancing model performance. This study delves into the combination of EfficientNet and Long Short-Term Memory (LSTM) neural networks tailored for a specialized dataset. While EfficientNet is renowned for its proficiency in adaptive scaling of convolutional networks, LSTM excels in understanding and retaining long-term dependencies in sequential data. Employing a relatively concise dataset, we initiated this experiment, aspiring to evaluate the potential of our unique model combination. Remarkably, the outcomes exceeded expectations, with our hybrid model showcasing a 100% score across precision, recall, F-measure, and accuracy metrics. Comparative evaluations further substantiated our model's superiority, outclassing several state-of-the-art counterparts on the same dataset. This paper provides comprehensive insights into the model's design, execution, and critical evaluation, emphasizing its strengths and potential in real-world applications. However, we also acknowledge the limitations presented by the short dataset, which could introduce risks of overfitting, potentially limiting the model's adaptability to broader contexts. Considering these findings, we project a future where extended datasets and iterative model refinements could set new benchmarks in the field.

Keywords: deep learning, LSTM, EfficientNet, hybrid models

Edition: Volume 12 Issue 11, November 2023,

Pages: 112 - 117