Human Activity Recognition Using EfficientNet-B2 Deep Learning Model

Human activity recognition refers to the automated process of categorizing and classifying human activities that is derived from sensor data or visual input. They are widely used in research, benchmarking various algorithms, and building real-world applications that benefit from understanding and pr...

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Bibliographic Details
Published in2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) pp. 1 - 4
Main Authors S, Arya P, Benifa, J.V. Bibal, Anu, K.P., Antony, Chippy Maria
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.12.2023
Subjects
Computational modeling
Deep learning
EfficientNet B2
Human activity recognition
Image classification
Machine learning
Satellite images
Task analysis
Training
Visualization
Online AccessGet full text
DOI10.1109/ICCEBS58601.2023.10449221

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Summary:Human activity recognition refers to the automated process of categorizing and classifying human activities that is derived from sensor data or visual input. They are widely used in research, benchmarking various algorithms, and building real-world applications that benefit from understanding and predicting human activity, such as healthcare, fitness tracking, assistive technology, and intelligent environments. EfficientNetB2 is well known for its superiority in performance and efficiency, making it an ideal option for tasks requiring HAR. This study demonstrates the efficacy of the EfficientNetB2 model in precisely classifying a range of human activities using data gathered from several sensors. Our goal is to evaluate the model's performance in classifying different human activities. The results clearly show that this model gave the highest accuracy of 75 percent for 9 epochs
DOI:10.1109/ICCEBS58601.2023.10449221