应用小波能量熵的人体活动时序自动标记方法

在基于可穿戴传感器的人体活动识别研究中采用的传统人工标记原始数据的方法步骤繁琐、效率低下，在一定程度上制约了相关研究的深入开展.为此，特提出一种基于小波能量熵的人体活动时间序列自动标记方法.该方法采用分布于人体躯干9处主要部位的多惯性测量单元同步采集17种人体活动加速度与角速度数据，通过滑窗对人体前腰部合加速度数据分段并使用多分辨率分析计算滑窗内小波能量熵，然后利用采集序列的时间约束选择初步分割阈值，对滑窗小波能量熵随时间变化曲线进行自动分割，并最终实现对6位受试人体活动时序数据的自动标记.结果表明，该方法的标记平均准确率为95.82%，总耗时约18.6 min，比人工标记平均耗时76.75 min减少75.76%，标记效率显著改善. 

Automatic Labeling Method for Human Motion Time Series Using Wavelet Energy Entropy

To enhance labeling efficiency of human motion time series, a new technique of automatic labeling for human motion time series was proposed.9 inertial measurement units placed on each of 6 subjects were used to acquire motion data from 17 human activities in terms of acceleration and angular velocity. Sliding window technique was adopted to segment motion data from each subject while multiresolution analysis was applied to calculate the corresponding wavelet energy entropy. A segmenting threshold and time constraints were chosen to label each subject''s motion data automatically. The results showed that the average labeling accuracy reached 95.82%. It took the proposed method approximately 18.6 minutes to label motion data of all 6 subjects, which was 75.76% shorter than the average labeling time by human, 76.75 minutes. The proposed method improved the labeling process significantly with relatively high average labeling accuracy.