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Related Concept Videos

Measurements of Strain01:27

Measurements of Strain

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Strain quantifies the deformation of a material under force, typically measured as normal strain, which represents the change in length when compared with the original length. Electrical strain gauges are used for enhanced accuracy. These devices consist of a conductive wire mounted on a paper backing that adheres to the material's surface. These gauges operate on the piezoresistive effect, where the wire's electrical resistance changes in response to mechanical deformation. The strain...
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Design Example: Strain Gauge Bridge or Wheatstone Bridge01:15

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The utilization of strain gauges as transducers for converting mechanical strain into electrical signals is a common practice in various engineering applications. These strain gauges are frequently integrated into Wheatstone bridge circuits to accurately measure parameters such as force or pressure. Within this context, each element within the circuit exhibits a resistance that undergoes subtle variations when subjected to mechanical strain. The primary objective is to convert minuscule...
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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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Updated: Aug 26, 2025

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Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces.

Taemin Kim1, Yejee Shin2, Kyowon Kang1

  • 1Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.

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|October 3, 2022
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Summary

This study introduces a new silent speech interface (SSI) using strain sensors and AI, achieving 87.53% accuracy in classifying 100 words. This novel approach overcomes limitations of traditional surface electromyography (sEMG) methods.

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Area of Science:

  • Engineering
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Wearable silent speech interfaces (SSI) enable communication without vocalization.
  • Surface electromyography (sEMG) is a common SSI method but faces challenges with signal quality and scalability.
  • Limitations of sEMG include poor signal-to-noise ratio and interelectrode interference.

Purpose of the Study:

  • To develop a novel SSI utilizing crystalline-silicon-based strain sensors and a 3D convolutional deep learning algorithm.
  • To address the scalability and signal quality issues associated with sEMG-based SSIs.
  • To demonstrate a reliable and accurate silent speech recognition system.

Main Methods:

  • Developed a novel SSI using crystalline-silicon-based strain sensors with minimized cell dimensions (<0.1 mm²).
  • Employed two perpendicularly placed strain gauges to capture biaxial strain information.
  • Integrated a 3D convolutional deep learning algorithm for data analysis.
  • Attached four strain sensors near the subject's mouth to collect strain data.

Main Results:

  • Achieved a high accuracy rate of 87.53% in classifying an unprecedentedly large wordset of 100 words.
  • Demonstrated the system's reliability through various analysis methods.
  • Compared performance with an sEMG-based SSI, which achieved a significantly lower accuracy rate of 42.60%.

Conclusions:

  • The novel SSI utilizing strain sensors and deep learning offers a reliable and highly accurate solution for silent speech recognition.
  • This approach overcomes the limitations of traditional sEMG-based SSIs, showing superior performance.
  • The developed system holds promise for advancing non-vocal communication technologies.