基于惯性传感器融合控制算法的聋哑手语识别

聋哑人如何与外界进行有效沟通一直是一个备受关注的难点问题。文中提出了一种基于惯性传感器融合控制算法的手语识别方案,旨在实现高效准确的实时手语识别。该融合控制算法采用反馈控制思想,对两种传统的姿态信息计算方法进行融合,减少了环境对传感器的影响,可以准确获取被测对象在瞬时状态下的姿态信息。该算法通过对自采的聋哑手语数据进行数据融合、数据预处理和特征提取等处理,利用支持向量机、K-近邻法和前馈神经网络分类器自适应模型集成的分类方法进行分类。结果显示,所提传感器融合控制算法有效地得出了实时姿态,该手语识别方案对30种聋哑拼音手语的识别准确率达到96.5%。所提方案将为聋哑人手语识别打下坚实的基础,并为传感器融合控制的相关研究提供参考。

Deaf Sign Language Recognition Based on Inertial Sensor Fusion Control Algorithm

How to effectively communicate with the outside world for deaf people has always been a difficult issue that has attracted much attention.This paper proposes a sign language recognition scheme based on inertial sensor fusion control algorithm,which aims to achieve efficient and accurate real-time sign language recognition.This fusion control algorithm uses feedback control ideas to fuse two traditional attitude information calculation methods,reduces the impact of the environment on sensors,and can accurately obtain the attitude information of the measured object in the transient state.The algorithm performs data fusion,data preprocessing and feature extraction on the collected deaf-mute sign language data,and uses an adaptive model integration method composed of support vector machines(SVM),K-nearest neighbors(KNN)and feedforward neural networks(FNN)for classification.The results show that the proposed sensor fusion control algorithm effectively obtains real-time poses.The sign language recognition scheme achieves accurate recognition of 30 deaf-mute pinyin sign languages with a recognition accuracy of 96.5%.The work of this paper will lay a solid foundation for sign language recognition of deaf and dumb people,and provide refe-rences for related research on sensor fusion control.