基于热释电红外传感器的人体动作识别方法

针对目前人体动作识别技术中存在的隐私暴露、技术复杂度高和识别精度低等相关问题，提出了一种基于热释电红外（PIR）传感器的人体动作识别方法。首先，采用一组安置在天花板上经过视场调制的PIR传感器采集人体运动时散发的红外热辐射信号，将传感器输出的电压模拟信号进行滤波放大后通过ZigBee无线模块传送到PC端打包成原始数据集；其次，将原始数据的两路传感器输出数据进行特征融合，对融合后的数据做标准化处理封装为训练集和测试集；然后，基于数据的特征提出一种两层级联的混合深度学习网络模型作为人体动作的分类算法，第一层采用一维卷积神经网络（1DCNN）对数据进行特征提取，第二层采用门控循环单元（GRU）保存历史输入信息防止丢失有效特征；最后，利用训练集来训练该网络模型得出参数最优的分类模型，通过测试集验证模型的正确性。实验结果表明，提出的该动作识别技术模型对基本动作分类的准确率高于98%，与图像动作识别或穿戴式设备动作识别相比，实现了实时、便捷、低成本和高保密性的高精度人体动作识别。

Human motion recognition method based on pyroelectric infrared sensor

Aiming at the privacy exposure, high technical complexity, and low recognition accuracy existing in the current human motion recognition technology, this paper proposed a human motion recognition method based on a pyroelectric infrared (PIR) sensor. Firstly, a set of PIR sensors placed on the ceiling and modulated by the field of view were used to collect the infrared heat radiation signal emitted by the human body when moving, and the voltage analog signal output by the sensor was filtered and amplified, and then transmitted to the PC through the ZigBee wireless module and packaged into raw data. Secondly, the two-way sensor output data of the original data feature was fused, and the fused data was standardized and packaged into training dataset and test dataset. Then, a two-layer cascaded hybrid deep learning network was proposed to be a classification algorithm of human motion based on the characteristics of the data. The first layer used one-dimensional convolutional neural network (1DCNN) to extract features from the data, and the second layer used gated recurrent unit (GRU) to save historical input information to prevent loss of valid features. Finally, the training dataset was used to train the network model to obtain a classification model with the best parameters, and the correctness of the model was verified through the test dataset. The experimental results show that the accuracy of the proposed motion recognition technology model for basic motion classification is higher than 98%. Compared with image motion recognition or wearable device motion recognition, it realizes high-precision human motion recognition with real-time, convenience, low cost and strong confidentiality.