Department of Artificial Intelligence, Pukyong National University, Busan 48513, Korea.
Softbrain Co. Ltd., Tokyo 103-0027, Japan.
Sensors (Basel). 2022 Aug 16;22(16):6113. doi: 10.3390/s22166113.
Hand gestures are a common means of communication in daily life, and many attempts have been made to recognize them automatically. Developing systems and algorithms to recognize hand gestures is expected to enhance the experience of human-computer interfaces, especially when there are difficulties in communicating vocally. A popular system for recognizing hand gestures is the air-writing method, where people write letters in the air by hand. The arm movements are tracked with a smartwatch/band with embedded acceleration and gyro sensors; a computer system then recognizes the written letters. One of the greatest difficulties in developing algorithms for air writing is the diversity of human hand/arm movements, which makes it difficult to build signal templates for air-written characters or network models. This paper proposes a method for recognizing air-written characters using an artificial neural network. We utilized uni-stroke-designed characters and presented a network model with inception modules and an ensemble structure. The proposed method was successfully evaluated using the data of air-written characters (Arabic numbers and English alphabets) from 18 people with 91.06% accuracy, which reduced the error rate of recent studies by approximately half.
手势是日常生活中一种常见的交流方式，人们已经尝试了许多方法来自动识别它们。开发用于识别手势的系统和算法有望增强人机界面的体验，特别是在难以进行语音交流时。一种流行的手势识别系统是空中书写方法，人们通过手动在空中书写字母。手臂运动由带有嵌入式加速度计和陀螺仪传感器的智能手表/手环进行跟踪；然后，计算机系统识别所写的字母。开发用于空中书写的算法的最大困难之一是人类手/臂运动的多样性，这使得难以为空中书写的字符或网络模型构建信号模板。本文提出了一种使用人工神经网络识别空中书写字符的方法。我们使用单笔画设计的字符，并提出了一种具有 inception 模块和集成结构的网络模型。所提出的方法使用 18 个人的空中书写字符（阿拉伯数字和英文字母）数据进行了成功评估，准确率为 91.06%，将最近研究的错误率降低了约一半。
