Sign Language Conversation Interpretation Using Wearable Sensors and
  Machine Learning
Basma Kalandar Ziemowit Dworakowski
The count of people suffering from various levels of hearing loss reached
1.57 billion in 2019. This huge number tends to suffer on many personal and
professional levels and strictly needs to be included with the rest of society
healthily. This paper presents a proof of concept of an automatic sign language
recognition system based on data obtained using a wearable device of 3 flex
sensors. The system is designed to interpret a selected set of American Sign
Language (ASL) dynamic words by collecting data in sequences of the performed
signs and using machine learning methods. The built models achieved
high-quality performances, such as Random Forest with 99% accuracy, Support
Vector Machine (SVM) with 99%, and two K-Nearest Neighbor (KNN) models with
98%. This indicates many possible paths toward the development of a full-scale
system.