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Sign Language Translation

Authors
  • Sinander, Pierre
  • Issa, Tomas
Publication Date
Jan 01, 2021
Source
DiVA - Academic Archive On-line
Keywords
Language
English
License
Green
External links

Abstract

The purpose of the thesis was to create a data glove that can translate ASL by reading the finger- and hand movements. Furthermore, the applicability of conductive fabric as stretch sensors was explored. To read the hand gestures stretch sensors constructed from conductive fabric were attached to each finger of the glove to distinguish how much they were bent. The hand movements were registered using a 3-axis accelerometer which was mounted on the glove. The sensor values were read by an Arduino Nano 33 IoT mounted to the wrist of the glove which processed the readings and translated them into the corresponding sign. The microcontroller would then wirelessly transmit the result to another device through Bluetooth Low Energy. The glove was able to correctly translate all the signs of the ASL alphabet with an average accuracy of 93%. It was found that signs with small differences in hand gestures such as S and T were harder to distinguish between which would result in an accuracy of 70% for these specific signs.  / Syftet med uppsatsen var att skapa en datahandske som kan översätta ASL genom att läsa av finger- och handrörelser. Vidare undersöktes om ledande tyg kan användas som sträcksensorer. För att läsa av handgesterna fästes ledande tyg på varje finger på handsken för att urskilja hur mycket de böjdes. Handrörelserna registrerades med en 3-axlig accelerometer som var monterad på handsken. Sensorvärdena lästes av en Arduino Nano 33 IoT monterad på handleden som översatte till de motsvarande tecknen. Mikrokontrollern överförde sedan resultatet trådlöst till en annan enhet via Bluetooth Low Energy. Handsken kunde korrekt översätta alla tecken på ASL-alfabetet med en genomsnittlig exakthet på 93%. Det visade sig att tecken med små skillnader i handgester som S och T var svårare att skilja mellan vilket resulterade i en noggrannhet på 70% för dessa specifika tecken. 

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