Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
Series Vol. 4 , 30 May 2023
Open
Access | Article
Sensor-based gesture recognition with convolutional neural networks
* Author to whom correspondence should be addressed.
Abstract
Sensor-based gesture recognition is an active research field of great significance with a wide range of applications in control systems for virtual reality, medical monitoring, and abnormal behavior determination. This problem has drawn the attention from both the academia and industry and many methods are proposed in the literature. Recently, deep learning has been widely applied in sensor-based gesture recognition and achieved good effects. In this paper, we proposed a classifier model based on Convolutional Neural Network (CNN) and applied it to EMG-based and smartphone-based datasets, respectively. For these two datasets, our model both achieved better classification accuracies than traditional machine learning models, with the results of approximately 97% and 72% accuracies, respectively. We also analyzed the effects of different parameters on the results of the proposed CNN model.
Keywords
sensor data, gesture recognition, convolutional neural network
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
- ISBN (Print)
- 978-1-915371-55-3
- ISBN (Online)
- 978-1-915371-56-0
- Published Date
- 30 May 2023
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/4/2023305
- Copyright
- 30 May 2023
- Open Access
- 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