在基于惯性传感器的人体行为识别研究中, 特征提取是其中的关键环节之一。而离散数据统计特征的稳定性依赖于特征提取的窗口大小。一般来说, 训练数据的窗口长度需要大于一个运动周期。因此, 针对测试数据远小于一个运动周期的短序列样本识别问题, 提出了一种基于模板匹配的新的解决方案。首先, 通过适当分割训练数据的长序列样本, 构建一个过完备的短时行为模板库, 将待测短时样本与模板库中样本进行一致化处理并进行匹配; 其次, 在匹配算法中, 采用样本间的F范数与整体梯度向量的2范数累加作为匹配度量准则, 得到相似度直方图; 最后, 基于相似度直方图, 根据投票策略得到最终分类识别结果。实验表明: 在使用单传感器识别短时行为的情况下, 新算法比传统算法在精度和稳定性上具有更好的性能, 并能适应不同窗口下短时行为分类问题。

In the study of human activity recognition(HAR) based on the inertial sensor, feature extraction was one of the key points. The stability of discrete data statistical features depended on the window size of feature extraction. Generally speaking, the length of window needed to be greater than one motion cycle. Therefore, compared to the traditional behavior recognition, short-time behavior recognition was more difficult. Thus a novel template matching method was proposed for recognizing the test samples whose durations were shorter than one motion cycle. Firstly, by properly dividing the long sequence samples, a complete short-time activity template library was constructed. The short-time samples to be tested and the samples in the template library were processed and matched. Secondly, in the matching algorithm, the similarity histogram was obtained by utilizing the sum of the F norm distance between the samples and the 2 norm distance of the global gradient vector as the matching metric. Finally, based on the similarity histogram, the final classification recognition results were obtained according to the voting strategy. Experiments show that in the case of using a single sensor to identify short-term behavior, the new algorithm had better performance than traditional algorithms in accuracy and stability, and can be adapted to short-term behavior classification problems under different windows.