本文首先介绍了一种基于智能手机传感器获取加速度等数据的App设计方法,设定了15种需要识别的动作、手机位置组合类别,收集了75万条运动数据记录;其次,采用滑动窗口技术分割时序数据,构建了有针对性的时域、频域特征指标,形成了不同窗口大小、步长的新样本系列;最后,通过分类结果筛选出性能优良的4类算法,证实了深度学习在特征构建方面的良好表现,探讨了滑动窗口大小、步长对识别结果的影响。这些成果对于构建人体行为识别系统具有一定参考价值。

Firstly,an App design method is designed,which is based on smart phone sensor to obtain acceleration and other data,sets 15 action and mobile phone position combination categories to be recognized,and collects 750 000 motion data records.Secondly,the sliding window technology is used to segment the time-series data,and the targeted time-domain and frequency-domain characteristic indexes are constructed to form a new sample series with different window sizes and steps.Finally,four kinds of algorithm with excellent performance are selected through the classification results,which demonstrates the good performance of deep learning in feature construction,and discusses the influence of sliding window size and step size on the recognition results.These results have reference value for the construction of human behavior recognition system.