Construction of a Dataset for Static and Dynamic Hand Tracking Using a Non-invasive Environment

Nogales, Rubén; Benalcázar, Marco E.; Toalumbo, Bryan; Palate, Alexis; Martínez, Ronnie; Vargas, Javier

doi:10.1007/978-981-33-4565-2_12

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…The LMC has three LED sensors and two depth cameras. Furthermore, this device retrieves the spatial positions, directions, and velocity of hands and fingers according to the coordinate axis, whose center is the center of the device [19] In addition, each user developed five gestures. Figure 3 shows the types of gestures.…”

Section: Dataset Buildingmentioning

confidence: 99%

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Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory

Nogales

Benalcázar

2023

BDCC

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Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensionality of the problem is directly related to the performance of machine learning models. The dimensionality problem can be addressed through feature selection and feature extraction. In this sense, the evaluation of a model with manual feature extraction and automatic feature extraction was proposed. The manual feature extraction was performed using the statistical functions of central tendency, while the automatic extraction was performed by means of a CNN and BiLSTM. These features were also evaluated in classifiers such as Softmax, ANN, and SVM. The best-performing model was the combination of BiLSTM and ANN (BiLSTM-ANN), with an accuracy of 99.9912%.

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Section: Dataset Buildingmentioning

confidence: 99%

“…In this context, the dataset was 8400 × 70. Each dataset instance had five fingers and three channels of X, Y, and Z data [19].…”

Section: Dataset Buildingmentioning

confidence: 99%

Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory

Nogales

Benalcázar

2023

BDCC

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Gamification Resources Applied to Reading Comprehension: Projects of Connection with Society Case Study

Páez-Quinde¹,

Pazmiño²,

Morocho-Lara³

et al. 2022

Lecture Notes in Networks and Systems

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Hand Gesture Recognition Using Leap Motion Controller, Infrared Information, and Deep Learning Framework

Toalumbo

Nogales

2022

Smart Technologies, Systems and Applications

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Construction of a Dataset for Static and Dynamic Hand Tracking Using a Non-invasive Environment

Cited by 8 publications

References 20 publications

Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory

Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory

Gamification Resources Applied to Reading Comprehension: Projects of Connection with Society Case Study

Hand Gesture Recognition Using Leap Motion Controller, Infrared Information, and Deep Learning Framework

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