基于改进Transformer-BiLSTM的人体活动识别模型

Human activity recognition model based on improved Transformer-BiLSTM

针对可穿戴传感器采集的时间序列往往具有维度高、噪声大等缺点导致活动识别方法准确率下降的问题,提出了基于改进Transformer-BiLSTM的人体活动识别模型。模型采用了Transformer编码器在处理长距离依赖和并行化计算方面的优势来提高序列特征提取的效率;随后将特征传递给添加了跳跃残差连接的双向长短期记忆网络,两次残差连接代替大量卷积层的同时保留了有效信息;提出了一种集成有时间信息编码的注意力层增强了模型的表达能力和对时序数据的理解能力。实验结果表明,该模型在公开数据集上的准确率达到了98.38%,有效提高了人体活动识别的准确率。

A human activity recognition model based on an improved Transformer-BiLSTM network is proposed to address the problem of decreased accuracy in activity recognition methods due to the high dimensionality and large noise of time series collected by wearable sensors.The model leverages the advantages of Transformer encoder in handling long-range dependencies and parallelized computations to enhance the efficiency of sequence feature extraction.Subsequently,the features are passed to a bidirectional long short-term memory network with skip residual connections,where two residual connections replace numerous convolutional layers while retaining essential information.Additionally,an attention layer integrated with time information encoding is proposed to enhance the model′s expressive power and understanding of temporal data.Experimental results show that the model achieves an accuracy of 98.38%on public datasets,effectively improving the accuracy of human activity recognition.