A CNN Bi-LSTM based Multimodal Continuous Hand Gesture Recognition

Nayan, Navneet; Ghosh, Debashis; Pradhan, Pyari Mohan

doi:10.1109/indiscon54605.2022.9862878

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…The other approach with higher accuracy than ours is [48], but only when using temporal difference as an input modality, which suggests temporal difference could provide valuable information for gesture recognition.…”

Section: B State-of-the-art Comparisonmentioning

confidence: 78%

“…Our proposed skeletonbased approach is compared to the approaches utilising various input modalities, where Seg, Flow and TD represent hand segmentation, optical flow and temporal difference, respectively. Temporal difference modality is proposed in [48] and it involves retrieving information from binarized absolute differences of RGB video frames. In [49], the authors extracted hand keypoints using MediaPipe from RGB video and employed the hand skeleton as the sole input modality, which is similar to our approach.…”

Section: B State-of-the-art Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

TY-Net: Transforming YOLO for Hand Gesture Recognition

Doždor,

Kalafatić,

Ban

et al. 2023

IEEE Access

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Section: B State-of-the-art Comparisonmentioning

confidence: 78%

Section: B State-of-the-art Comparisonmentioning

confidence: 99%

TY-Net: Transforming YOLO for Hand Gesture Recognition

Doždor,

Kalafatić,

Ban

et al. 2023

IEEE Access

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“…Separate T-test analyses were performed to determine the accuracy of both methods. The accuracy of the Hand Gesture Recognition algorithm and Convolutional Neural Network (CNN) was evaluated, and the results were compared using mean accuracy [11]. The independent t-test analyses were carried out to compute the accuracy of both methods.…”

Section: Discussionmentioning

confidence: 99%

Enhance the AI Virtual System Accuracy with Novel Hand Gesture Recognition Algorithm Comparing to Convolutional Neural Network

Sadiq,

Saraswathi

2024

E3S Web Conf.

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The objective of this study is to enhance the precision of AI virtual systems by implementing Novel Hand Gesture Recognition techniques in comparison to Convolutional Neural Network. Materials and Methods: To recognise hand motions, a Convolutional Neural Network with distinct training and testing stages is utilized. The average Gpower for the test is between 0.05 and 0.85, or around 85%. Sample size is determined as 27,455 for each group using G Power 3.1 software (G Power setting parameters: α=0.05 and power=0.85). Results and Discussion: Novel Hand gesture recognition 92.60% identifies between objects and boosts the observed accuracy with a statistically non-significant value of p=0.123 (p>0.05) in comparison to the convolutional neural network's 88.59%. Conclusion: Comparison of the Novel Hand gesture Recognition algorithm and Convolutional Neural Network in terms of performance that shows Hand gesture recognition has 91.62% with better accuracy.

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