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Impaired Speech Assistance Glove Project

The project aims to help speech-impaired individuals with a glove device using machine learning technology. Components include flex and pressure sensors, ADC, accelerometers, Bluetooth, microcontroller, and Li-ion batteries. Development involves Android vs. iPhone software considerations and machine learning algorithms like Hidden Markov Models. Budget secured through Boeing Sponsorship. Challenges include complexity of implementing algorithms and Bluetooth technology. Approach includes research, hand gesture smoothing, and Bluetooth research or switching to classic Bluetooth. Questions? Contact us for more information.

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Impaired Speech Assistance Glove Project

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  1. High 6 CDR

  2. Group Members • Kirk Chan • Brian Troili • Ali Mizan • Laura Rubio-Perez

  3. Project Introduction

  4. Motivation • Fresh idea to the UCF community • This project has the potential to help the speech impaired • Based on the research, technologies necessary were interesting • Machine learning • Personal taste

  5. Goals We want the following key factors:

  6. Specifications

  7. Design Approach

  8. Design Overview

  9. Hardware Components

  10. Hardware Components • Flex Sensors • Able to detect changes in bend/flex • Changes its resistance at several points along the device • When a current is applied, it creates a voltage divider

  11. Hardware Components • Pressure Sensors • Acts as a force sensing resistor • When the sensor is unloaded, its resistance is very high • When pressure is applied, its resistance decreases

  12. Hardware Components Analog/Digital Converter (ADC) • Serial communication preferred. • Large number of input channels. • Avoid serial address conflict. - ADS7828 • I2C compatible • 8 Channel ADC • variable I2C address

  13. Hardware Components Analog/Digital Converter (ADC) • Serial communication preferred. • Large number of input channels. • Avoid serial address conflict. - ADS7828 • I2C compatible • 8 Channel ADC • variable I2C address

  14. Hardware Components Accelerometer and Gyroscope • Inertial Measurement Unit (IMU) • Speed demand allow for serial buses. - ITG3200/ADXL345 combo board • 3.3V input • I2C compatible • 3 axis each • calibrate to 2, 4, 8, and 16g

  15. Hardware Components Wireless Communication

  16. Hardware Components Bluetooth Low Energy (BLE) • Low power consumption • Approx. 50m range Wireless Communication

  17. Hardware Components BLE TTL Transceiver • Bluetooth v4.0 • 3.3V input voltage • Approximately $6 • Customizable Baud Rate

  18. Hardware Components • Microcontroller

  19. Hardware Components • Microcontroller

  20. Hardware Components • Development Environment

  21. Hardware Components • Development Environment

  22. Hardware components • Li-ion Batteries • Small size and lightweight • High energy density • Capacity gradually declines • Can drop below regulated voltage

  23. Hardware components • Disadvantages: • Voltage ripple • Complexity of external passive • components on board • Switching Regulator Advantages: • Efficiency • Minimal power dissipated • Minimal switch duty-cycle

  24. Hardware components • Step down switching regulator • Vin range: 4V - 40V • Vout range: 3.3V - 37V • LM2576 Switching Regulator:

  25. PCB

  26. Software Components

  27. Android vs iPhone • Android • Can be developed on Windows, Mac, and Linux • Apps written in Java • iPhone • Can only be developed in Mac • Apps written in objective C • Apple development software only works with other apple development software

  28. Android IDEs IntelliJ (free version) • Advantages: • Less buggy • More intuitive • Faster • Better GUI • Disadvantages: • Java, Groovy, or Scala are only 3 languages supported in free version Eclipse • Advantages: • More plug-ins available • More commonly used • Disadvantages: • Has bugs and crashes a lot

  29. Application Features

  30. Application Features

  31. Class Diagram

  32. Software Components • Two main components: • Android Application • is the interface between the user and the machine learning algorithm • Takes in raw data from glove • Displays letter on screen • Translator • There is no way to learn every single sign language gesture with 100% accuracy. • Machine learning gives ~95% accuracy. • Uses learning algorithm to learn from examples

  33. Machine Learning • General Overview • Uses data in order to approximate target function • Uses examples to determine which hypothesis is closest approximation of unknown target function • 3 popular types • Regression • Classification • Clustering

  34. Machine Learning Algorithm chosen Facts and image taken from IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 12, DECEMBER 1998 • Advantage of Hidden Markov Model • No need for segmentation • Very robust towards small changes in motion • History of being used for language recognition

  35. Budget

  36. Financing up to date • Boeing Sponsorship

  37. Progress

  38. Issues • The Hidden Markov Model is very complicated to both understand and implement • Training the algorithm • Varying hand sizes • BLE is relatively new and requires more research

  39. Approaching the issues • Implementation of Hidden Markov • Reading and researching • Varying Hand size issue • Smooth the trajectory, hand shape, and orientation • Creating tolerances for hand gestures (for flexion) • BLE • Research or switching to classic Bluetooth (version 3.0)

  40. Questions?

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