Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.09041 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929755522072576 |
|---|---|
| author | Wazir, Hafsa Ahmad, Jawad Khan, Muazzam A. Jan, Sana Ullah Khan, Fadia Ali Khan, Muhammad Shahbaz |
| author_facet | Wazir, Hafsa Ahmad, Jawad Khan, Muazzam A. Jan, Sana Ullah Khan, Fadia Ali Khan, Muhammad Shahbaz |
| contents | Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DB1 dataset. The proposed model boasts an accuracy of 99.7\% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen's kappa coefficient, Matthew's correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09041 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition Wazir, Hafsa Ahmad, Jawad Khan, Muazzam A. Jan, Sana Ullah Khan, Fadia Ali Khan, Muhammad Shahbaz Cryptography and Security Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a Gated Recurrent Unit (GRU). The proposed model is trained on the complete Ninapro DB1 dataset. The proposed model boasts an accuracy of 99.7\% in classifying 53 classes in just 25 milliseconds. In addition to being fast, the proposed model is lightweight with just 3,946 KB in size. Moreover, the proposed model has also been evaluated for the reliability parameters, i.e., Cohen's kappa coefficient, Matthew's correlation coefficient, and confidence intervals. The close to ideal results of these parameters validate the models performance on unseen data. |
| title | A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2503.09041 |