结合CNN-BiLSTM-SA运动模式识别的自适应步频检测方法

Adaptive step detection method combining CNN-BiLSTM-SA motion pattern recognition

随着位置服务(LBS)的普及,基于智能手机的行人步频检测方法对行人航迹推算(PDR)有重要影响.针对传统步频检测方法在行人多种运动模式下计步误差大的问题,提出一种结合CNN-BiLSTM-SA运动模式识别的自适应步频检测方法.首先根据行人行走特点划分运动模式,使用卷积神经网络(CNN)提取行人不同运动模式的局部特征,利用自注意力机制(SA)对提取的运动特征进行权重分配;再结合双向长短期记忆网络(BiLSTM)挖掘行人运动特征的前后时序关系进行分类识别;然后根据分类结果提出自适应最小峰距和自适应动态阈值两个特征约束的峰值检测算法对步频进行检测,并在步行中动态调整阈值大小.实验结果表明:本文提出方法在8种组合运动模式下步频检测平均误差率为1.31%,与传统峰值检测相比误差率降低5.97%,同时也优于固定阈值法.

With the popularity of location based services(LBS),smartphone-based pedestrian step detection methods have important impacts on pedestrian dead reckoning(PDR).We propose an adaptive step detection method combining CNN-BiLSTM-SA motion pattern recognition to address the problem that traditional methods have large step counting errors under multiple pedestrian motion patterns.Firstly,the motion patterns are classified according to the walking characteristics of pedestrians,and the local features of different motion patterns of pedestrians are extracted by using convolutional neural network(CNN),and the weights of the extracted motion features are assigned by using self-attention(SA)mechanism,and then the bidirectional long short term memory(BiLSTM)network is combined to mine the pre-post temporal relationship of pedestrian motion features for classification and recognition.Then the peak detection algorithm with two feature constraints,adaptive minimum peak distance and adaptive dynamic threshold,is proposed to detect the step frequency according to the classification results,and the threshold size is dynamically adjusted in walking.The experimental results show that the average error rate of the proposed method for step frequency detection under eight combined motion patterns is 1.31%,which is 5.97%lower than that of the traditional peak detection,and also better than the fixed threshold method.