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.

本文介绍了一个基于可穿戴设备的自动手语识别系统的概念验证，该系统通过采集动态手语的数据序列并使用机器学习方法来解释一组美国手语（ASL）动态单词。建立的模型达到了高质量的性能，如随机森林模型准确率达到了99％，支持向量机（SVM）和两个K最近邻（KNN）模型准确率均为98％，这表明了一个全面系统发展的许多可能路径。

使用可穿戴传感器和机器学习的手语对话翻译