Human activity recognition based on sensors (e.g., accelerometer and gyroscope) embedded in smartphones is of great significance for many applications under uncontrolled environments. Although significant progress has been noticed in this field, one of the challenges limiting its real-life applications lies in robust feature extraction for efficient activity recognition on smartphones. This study addresses this challenge by proposing an improved bag-of-words representation for activity signal characterization. Specifically, raw activity signals are processed by discrete wavelet transformation to extract local features, which will be clustered using K-means to form a bag-of-words dictionary. The vocabularies in the dictionary are regarded as bin centers for histogram feature construction. For each local feature of an activity signal, its distance from all the bin centers will be measured. To capture higher-order information for feature representation, the frequency for the bin centers corresponding to the minimum n distances will be updated. Moreover, the frequency is increased by a trigonometry constraint cosine value of the corresponding distances to account for activity signals’ structural information. The proposed feature representation has been verified with three well-established classifiers, namely SVM, ANN, and KNN on the UCI-HAR dataset. The consistently good performance validates the effectiveness and robustness of the proposed feature representation. Compared with the state-of-the-art, the experimental results also demonstrate the advantage of the proposed method in terms of accuracy and computational cost.

基于嵌入在智能手机中的传感器（例如，加速度计和陀螺仪）的人类活动识别对于不受控制的环境下的许多应用具有重要意义。尽管在该领域已经取得了显着进步，但限制其实际应用的挑战之一是要在智能手机上进行有效活动识别的强大特征提取功能。这项研究通过提出一种用于活动信号表征的改进的词袋表示法来应对这一挑战。具体来说，原始活动信号通过离散小波变换处理，以提取局部特征，这些局部特征将使用K均值进行聚类以形成词袋词典。字典中的词汇被视为直方图特征构建的bin中心。对于活动信号的每个局部特征，距所有垃圾箱中心的距离将被测量。为了捕获用于特征表示的高阶信息，bin中心的频率对应于最小值n个距离将被更新。此外，通过对应距离的三角约束余弦值来增加频率，以考虑活动信号的结构信息。在UCI-HAR数据集上，已使用三个完善的分类器（即SVM，ANN和KNN）验证了所提出的特征表示。始终如一的良好性能验证了所提出特征表示的有效性和鲁棒性。与最新技术相比，实验结果还证明了该方法在准确性和计算成本方面的优势。