Comparison between handcraft feature extraction and methods based on Recurrent Neural Network models for gesture recognition by instrumented gloves: A case for Brazilian Sign Language Alphabet

Research article (Biomedical Signal Processing and Control, 2022) · cited 15× · AI/ML
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Comparison between handcraft feature extraction and methods based on Recurrent Neural Network models for gesture recognition by instrumented gloves: A case for Brazilian Sign Language Alphabet

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Comparison between handcraft feature extraction and methods based on Recurrent Neural Network models for gesture recognition by instrumented gloves: A case for Brazilian Sign Language Alphabet is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Comparison between handcraft feature extraction and methods based on Recurrent Neural Network models for gesture recognition by instrumented gloves: A case for Brazilian Sign Language Alphabet. Retrieved May 24, 2026, from https://4ort.xyz/entity/comparison-between-handcraft-feature-extraction-and-methods-based-on-recurrent-neural-network-models-for-gesture-recogni
MLA “Comparison between handcraft feature extraction and methods based on Recurrent Neural Network models for gesture recognition by instrumented gloves: A case for Brazilian Sign Language Alphabet.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/comparison-between-handcraft-feature-extraction-and-methods-based-on-recurrent-neural-network-models-for-gesture-recogni.
BibTeX @misc{4ortxyz_comparison-between-handcraft-feature-extraction-and-methods-based-on-recurrent-neural-network-models-for-gesture-recogni_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Comparison between handcraft feature extraction and methods based on Recurrent Neural Network models for gesture recognition by instrumented gloves: A case for Brazilian Sign Language Alphabet}}, year = {2026}, url = {https://4ort.xyz/entity/comparison-between-handcraft-feature-extraction-and-methods-based-on-recurrent-neural-network-models-for-gesture-recogni}, note = {Accessed: 2026-05-24}}
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