PDF(Pubmed)
Abstract：
The aim of this study is to develop a practical software solution for real-time recognition of sign language words using two arms. This will facilitate communication between hearing-impaired individuals and those who can hear. We are aware of several sign language recognition systems developed using different technologies, including cameras, armbands, and gloves. However, the system we propose in this study stands out for its practicality, utilizing surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. We address the drawbacks of other methods, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have limited their practical application. Our software can run on different operating systems using digital signal processing and machine learning methods specific to this study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The random forest algorithm emerged as the most successful, achieving a remarkable 99.875% accuracy, while the naïve Bayes algorithm had the lowest success rate with 87.625% accuracy. The new system promises to significantly improve communication for people with hearing disabilities and ensures seamless integration into daily life without compromising user comfort or lifestyle quality.
摘要：
本研究的目的是开发一种实用的软件解决方案，用于使用两个手臂实时识别手语单词。这将促进听力受损者与能听见者之间的交流。我们知道使用不同技术开发的几种手语识别系统，包括摄像头,臂章,和手套。然而,我们在这项研究中提出的系统以其实用性而脱颖而出，利用两臂的表面肌电图（肌肉活动）和惯性测量单元（运动动力学）数据。我们解决了其他方法的缺点，比如高成本，由于环境光和障碍物的低精度，和复杂的硬件要求，这限制了它们的实际应用。我们的软件可以使用本研究特有的数字信号处理和机器学习方法在不同的操作系统上运行。对于测试，我们根据其在日常生活中的使用频率创建了一个包含80个单词的数据集，并进行了彻底的特征提取过程。我们使用各种分类器和参数测试了识别性能，并比较了结果。随机森林算法是最成功的，达到惊人的99.875%的准确度，而朴素贝叶斯算法的成功率最低，准确率为87.625%。新系统有望显着改善听力障碍者的沟通，并确保无缝集成到日常生活中，而不会影响用户的舒适度或生活质量。
