基于GRU-DRSN的双通道人体活动识别

人体活动识别(human activity recognizition, HAR)在医疗、军工、智能家居等领域有很大的应用空间。传统机器学习方法特征提取难度较大且精度不高。针对上述问题并结合传感器时序特性，提出了一种融合CBAM(convolutional block attention module)注意力机制的GRU-DRSN双通道并行模型，有效避免了传统串行模型因网络深度加深引起梯度爆炸和消失问题。同时并行结构使得两条支路具有相同的优先级，使用深度残差收缩网络(deep residual shrinkage network, DRSN)提取数据的深层空间特征，同时使用门控循环结构(gated recurrent unit, GRU)学习活动样本在时间序列上的特征，同时进行提取样本不同维度的特征，并通过CBAM模块进行特征的权重分配，最后通过Softmax层进行识别，实现了端对端的人体活动识别。使用公开数据集(wireless sensor data mining, WISDM)进行验证，模型平均精度达到了97.6%,与传统机器学习模型和前人所提神经网络模型相比，有更好的识别效果。

Human Activity Recognition Based on GRU-DRSN with Dual Channels

Human activity recognition (HAR) has a wide range of applications in medical, military, smart home, and other fields. Feature extraction using traditional machine learning methods is challenging and imprecise. Aiming to address the aforementioned issues and leveraging the timing characteristics of the sensor, we propose a DRSN-GRU model that integrates the CBAM attention mechanism. The problem of gradient explosion and vanishing, which is caused by increasing network depth in traditional sequential models, is effectively avoided. The parallel structure allows both branches to be given the same priority. The Deep Residual Shrinkage Network (DRSN) was used to extract the deep spatial characteristics of the data, while the Gated Recurrent Unit (GRU) was used to learn the characteristics of the activity samples over time series. The two channels simultaneously extract features of different dimensions from the samples and distribute the weight of these features using the CBAM module. Finally, the end-to-end human activity recognition is achieved through the Softmax layer for classification. The WISDM public data set was used for verification, and the model achieved an average accuracy of 97.6%. This demonstrates a superior recognition effect compared to the traditional machine learning and neural network models proposed by previous researchers.