A sign language recognition (SLR) system has been broadly used by deaf individuals as a communicative tool. Progress of SLR systems paves the way for the development of Human–computer interaction (HCI) since sign language is the most structured form, and every movement has a specific meaning. In this paper, a wearable low-cost device based on surface electromyography (sEMG) and Inertial Measurement Unit (IMU) sensors was designed and fabricated. The fusion of these two sensors will improve the system’s accuracy to capture signs. In this work, sEMG and IMU recordings were collected from ten volunteers while performing 20 commonly used Persian Sign Language (PSL) signs ten times in defined time gaps. To make the proposed algorithm computationally efficient, the 25 highest-ranked features of two modalities (sEMG, IMU) were extracted and classified by the KNN classifier that achieved 96.13% average accuracy. These results demonstrate the feasibility of our purposed method for PSL recognition.

聋人已广泛使用手语识别（SLR）系统作为交流工具。SLR系统的进步为人机交互（HCI）的发展铺平了道路，因为手语是最结构化的形式，并且每个动作都有特定的含义。本文设计并制造了一种基于表面肌电图（sEMG）和惯性测量单元（IMU）传感器的可穿戴低成本设备。这两个传感器的融合将提高系统捕获信号的准确性。在这项工作中，从十位志愿者那里收集了sEMG和IMU录音，同时在定义的时间间隔内执行了20次常用的波斯手语（PSL）手势十次。为了使所提出的算法具有更高的计算效率，两种模态（sEMG，IMU）由KNN分类器提取并分类，平均准确率达到96.13％。这些结果证明了我们有目的的PSL识别方法的可行性。