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Cited by 67Muscle activation and electromyography studiesHand Gesture Recognition SystemsEmotion and Mood Recognition

An improved performance of deep learning based on convolution neural network to classify the hand motion by evaluating hyper parameter

Triwiyanto, Triwiyanto, Pawana, I. Putu Alit, Purnomo, Mauridhi Hery·2020·IEEE Transactions on Neural Systems and Rehabilitation Engineering
DOI 10.1109/tnsre.2020.2999505
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Abstract

High accuracy in pattern recognition based on electromyography(EMG) contributes to the effectiveness of prosthetics hand development. This study aimed to improve performance and simplify the deep learning pre-processing based on the convolution neural network (CNN) algorithm for classifying ten hand motion from two raw EMG signals. The main contribution of this study is the simplicity of pre-processing stage in classifier machine. For instance, the feature extraction process is not required. Furthermore, the performance of the classifier was improved by evaluating the best hyperparameter in deep learning architecture. To validate the performance of deep learning, the public dataset from ten subjects was evaluated. The performance of the proposed method was compared to other conventional machine learning, specifically LDA, SVM, and KNN. The CNN can discriminate the ten hand-motion based on raw EMG signal without handcrafts feature extraction. The results of the evaluation showed that CNN outperformed other classifiers. The average accuracy for all motion ranges between 0.77 and 0.93. The statistical t-test between using two-channel(CH1 and CH2) and single-channel(CH2) shows that there is no significant difference in accuracy with p-value >0.05. The proposed method was useful in the study of prosthetic hand, which required the simple architecture of machine learning and high performance in the classification.

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  • The choice of 1D-CNN kernel size normally falls in the range of 1 × 3 to 1 × 5, with exceptions of 1 × 8, 2 × 101, and 1 × 20 in some studies.

    “The choice of 1D-CNN kernel size normally falls in the range of 1 × 3 to 1 × 5 (with exceptions in [22,63,64] where kernels of size 1 × 8, 2 × 101, and 1 × 20 are adopted).”

    From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [63]

  • A majority of CNNs consist of five to nine layers, usually including two to three convolutional layers, two to three max-pooling layers, followed by one to two fully connected layers before the output layer.

    “A majority of the CNNs consist of five to nine layers [23,63,64,113,114,165,166,167,168], usually including two to three convolutional layers, two to three max-pooling layers, followed by one to two fully connected layers before feeding the feature representation into the output layer (softmax layer in most cases).”

    From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [63]

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Authors
Triwiyanto, T., Pawana, I. P. A., & Purnomo, M. H.
Affiliations
  • Poltekkes Kemenkes Surabaya (ID)
  • Rumah Sakit Dokter Soetomo (ID)
  • Institut Teknologi Sepuluh Nopember (ID)
ORCID
Venue
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-07
Volume
28
Issue
7
Pages
1678-1688
ISSN
  • 1534-4320 (ISSN-L)
  • 1558-0210
DOI
10.1109/tnsre.2020.2999505
OpenAlex
W3033182043
Cited by
67
References
39
Citations by year
2026: 4 · 2025: 8 · 2024: 12 · 2023: 12 · 2022: 19 · 2021: 9 · 2020: 3
FWCI
3.38
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Keywords
electromyography, hand motion classification, convolutional neural networks, hyperparameter optimization, prosthetic hand, feature extraction, EMG-based control
MeSH terms
Machine Learning, Deep Learning, Electromyography, Hand, Neural Networks, Computer

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