融合决策下的数字序列跌倒检测方法

Fall Detection Method of Digital Sequence Based on Fusion Strategy

跌倒已成为老年人因伤致残的首要原因,及时准确地对跌倒事件示警是救助工作中非常重要的一环。为提高跌倒检测的准确率,提出了一种兼顾高维数字序列整体性和不同维度特异性的融合决策下的跌倒检测方法。对获取于手腕便携式传感器的输入数字序列根据合加速度的显著性进行窗口分割,保证数据时序性的同时提高跌倒信息的可辨识性。引入气压差字段和体温字段,建立具有九轴特征的归一化数字序列,探索更多与跌倒检测相关的信息。根据集成学习中具有互补性的梯度提升决策树(gradient boosting decision tree,GBDT)模型和随机森林(random forest,RF)模型的回归分类结果进行融合决策,得出跌倒事件是否发生的分类判别。实验结果显示,本文方法在自测数据上具有比单一采用GBDT模型和RF模型更高的跌倒检测准确率,在UR Fall和UMA Fall公开数据集上,文中提出的融合策略同样具有优异的跌倒检测准确率,说明了该方法的有效性和泛化能力。

Falls have become the primary cause of disability due to injury for the elderly.Timely and accurate warning of fall events is an important link to rescue work.In order to improve the accuracy of fall detection,a fall detection method based on a fusion strategy is proposed,which considers both the integrity of high-dimensional digital sequences and the specificity of different dimensions.The input digital sequences obtained from the wrist portable sensor are processed by window segmentation according to the saliency of resultant acceleration,so as to ensure the timing of the data and improve the identifiability of the fall information.The features of air pressure difference and body temperature are introduced to formulate a normalized digital sequence with nine-axis characteristics and explore more information related to fall detection.According to the regression classification results of gradient boosting decision tree(GBDT)and random forest(RF)in ensemble learning,the fusion strategy is considered to obtain the classification identification of whether fall events have happened.Experimental results illustrate that the proposed method achieves higher accuracy of fall detection than the GBDT model and the RF model on self-testing data.Moreover,the proposed fusion strategy also achieves an excellent accuracy of fall detection in the UR Fall and UMA Fall public datasets,validating the effectiveness and generalization of the proposed method.