Medical Gesture Recognition Method Based on Improved Lightweight Network

Wang, Wenjie; He, Mengling; Wang, Xiaohua; Ma, Jianwei; Song, Huajian

doi:10.3390/app12136414

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Table 1 presents the summary of the related works. [11] AlexNet, VGG16 Wang et al (2022) [12] E-MobileNetv2 Gadekallu et al (2022) [13] CNN + HHO Li et al (2022) [14] HOG, 9ULBP SVM Sahoo et al (2022) [11] proposed a score-level fusion technique between AlexNet and VGG16 for hand gesture recognition. In the effort of fine-tuning both CNN models, weights are transferred from the pre-trained model for initialization instead of starting from scratch.…”

Section: Related Workmentioning

confidence: 99%

“…Wang et al (2022) [12] proposed an improved lightweight CNN model by adding adaptive channel attention (ECA module) to an existing MobileNetv2 CNN model called E-MobileNetv2. The newly added module helped reduce the interference of unrelated information so as to enhance the model's feature refining ability, especially in capturing the cross-channelled interactions.…”

Section: Related Workmentioning

confidence: 99%

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Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier

et al. 2022

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Gesture recognition has been studied for a while within the fields of computer vision and pattern recognition. A gesture can be defined as a meaningful physical movement of the fingers, hands, arms, or other parts of the body with the purpose to convey information for the environment interaction. For instance, hand gesture recognition (HGR) can be used to recognize sign language which is the primary means of communication by the deaf and mute. Vision-based HGR is critical in its application; however, there are challenges that will need to be overcome such as variations in the background, illuminations, hand orientation and size and similarities among gestures. The traditional machine learning approach has been widely used in vision-based HGR in recent years but the complexity of its processing has been a major challenge—especially on the handcrafted feature extraction. The effectiveness of the handcrafted feature extraction technique was not proven across various datasets in comparison to deep learning techniques. Therefore, a hybrid network architecture dubbed as Lightweight VGG16 and Random Forest (Lightweight VGG16-RF) is proposed for vision-based hand gesture recognition. The proposed model adopts feature extraction techniques via the convolutional neural network (CNN) while using the machine learning method to perform classification. Experiments were carried out on publicly available datasets such as American Sign Language (ASL), ASL Digits and NUS Hand Posture dataset. The experimental results demonstrate that the proposed model, a combination of lightweight VGG16 and random forest, outperforms other methods.

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