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Related Concept Videos

Language01:16

Language

920
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
807
Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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Related Experiment Video

Updated: Feb 11, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors.

Seongjung Kim, Jongman Kim, Soonjae Ahn

    Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
    |May 2, 2018
    PubMed
    Summary

    This study introduces an ensemble artificial neural network (E-ANN) for finger language recognition using EMG sensors. The E-ANN system significantly improves accuracy compared to traditional methods, aiding communication for the deaf.

    Keywords:
    Finger language recognitionarmband sensorsurface electromyography (EMG)

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Deaf individuals face communication barriers, leading to social and financial disadvantages.
    • Specialized communication methods like sign and finger languages can limit interaction.
    • Developing assistive technologies is crucial for inclusivity.

    Purpose of the Study:

    • To develop a novel finger language recognition algorithm.
    • To utilize an ensemble artificial neural network (E-ANN) for enhanced accuracy.
    • To employ an armband system with electromyography (EMG) sensors for data acquisition.

    Main Methods:

    • Signal acquisition, filtering, segmentation, and feature extraction were performed.
    • An E-ANN classifier was trained and evaluated using Korean finger language data.
    • Performance was assessed using 5-fold cross-validation and compared against a standard artificial neural network (ANN).

    Main Results:

    • Increasing the number of E-ANN classifiers and training data size improved average accuracy.
    • Higher classifier counts (up to 8) and data size (300) reduced accuracy variability.
    • The optimal E-ANN configuration demonstrated significantly higher accuracy than a general ANN.

    Conclusions:

    • The developed E-ANN finger language recognition system offers superior performance.
    • Optimized E-ANN models enhance accuracy and reliability in recognizing finger gestures.
    • This technology holds potential for reducing communication barriers for the deaf community.