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Related Concept Videos

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The sign test is an important tool in nonparametric statistics, offering a straightforward yet effective method for analyzing matched pairs, nominal data, or hypotheses concerning the median of a population. It transforms data points into positive or negative signs, avoiding the need for assumptions about data distribution and instead focusing on the direction of change. It is particularly valuable when data does not conform to the normal distribution requirements of many parametric tests. For...
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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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Sign Test for Nominal Data01:12

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The sign test is a nonparametric method used to evaluate hypotheses about the median of a single sample or to compare the medians of two related samples. The sign test is particularly useful when dealing with nominal data, which includes distinct categories without an inherent order, such as names, labels, and preferences. Nominal data restricts statistical analysis to evaluating population proportions rather than mean or median values that require continuous data.
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Sign Test for Median of Single Population01:20

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In general, the sign test serves as a nonparametric method to test hypotheses about the median of a single population when the data does not follow a known distribution. This simplicity makes it particularly useful for small sample sizes or when the assumptions of parametric tests cannot be met. The process begins with identifying a null hypothesis, typically stating that the population median equals a specific value. The alternative hypothesis could be that the median is either not equal to,...
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When analyzing a beam subjected to various loads, it is crucial to understand the internal forces and moments generated within the structure. These internal forces can be broadly classified into normal forces, shear forces, and bending moments. To determine these forces and moments, we use the method of sections and apply a specific sign convention based on their direction and the side of the section being analyzed.
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The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
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Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
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LSWH100: A handshape dataset for Brazilian sign language (Libras) using SignWriting.

Vicente Coelho Lobo-Neto1, Helio Pedrini1

  • 1Institute of Computing, University of Campinas, Av. Albert Einstein 1251, Campinas, SP 13083-852, Brazil.

Data in Brief
|August 30, 2024
PubMed
Summary
This summary is machine-generated.

A new dataset, Libras SignWriting Handshape (LSWH100), offers 144,000 synthetic images for Brazilian Sign Language (Libras) recognition. This resource aids in advancing sign language technologies.

Keywords:
Computer visionHand configuration recognitionHand keypointsHandshape recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Linguistics

Background:

  • Sign language recognition is crucial for communication accessibility.
  • Existing datasets may lack diversity in handshapes and real-world conditions.
  • Brazilian Sign Language (Libras) requires specialized resources for accurate recognition.

Purpose of the Study:

  • Introduce the Libras SignWriting Handshape (LSWH100) dataset.
  • Provide a comprehensive resource for training and evaluating sign language recognition models.
  • Facilitate research in handshape classification, detection, segmentation, and 3D pose estimation for Libras.

Main Methods:

  • Generated 144,000 synthetic images using Blender.
  • Included 100 distinct handshape classes from Brazilian Sign Language (Libras).
  • Annotated images with classification, detection, segmentation, depth, and 3D keypoints.

Main Results:

  • Created a diverse dataset with variations in size, rotation, skin tone, and scene conditions.
  • The dataset features handshapes named using the SignWriting convention.
  • LSWH100 presents a challenging benchmark for sign language recognition tasks.

Conclusions:

  • The LSWH100 dataset is a valuable resource for advancing Libras recognition.
  • This dataset has the potential to significantly improve the performance of sign language recognition systems.
  • The availability of LSWH100 can positively impact communication for the Deaf community.