Li Yun, Popescu Mihail, Ho K C
ECE Dept., University of Missouri, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5867-70. doi: 10.1109/EMBC.2012.6347328.
Falls represent an important health problem for older adults. This issue continues to generate interest in the research and development of fall detection systems. In previous work we proposed an acoustic fall detection system (acoustic-FADE) that employs an 8-microphone circular array to automatically detect falls. Acoustic-FADE has achieved encouraging results: 100% detection at 3% false alarm rate in laboratory tests. In this paper, we use a dataset from previous work to investigate how to further improve AFADE performance. To analyze the relationship between fall and non-fall signatures we used the improved visual assessment of tendency (iVAT) clustering algorithm in conjunction with a nearest neighbor based distance to find the most challenging false alarms. Then, we employed a genetic algorithm (GA) framework to perform feature selection and find the mel-frequency cepstral coefficients (MFCC) that improve the classification performance. We found that using only three MFCC coefficients (1, 28, 29) instead of our previous choice (1,2,3,4,5,6) improves the classification performance.
跌倒对老年人来说是一个重要的健康问题。这个问题持续引发人们对跌倒检测系统研发的兴趣。在之前的工作中，我们提出了一种声学跌倒检测系统（acoustic - FADE），它采用一个8麦克风圆形阵列来自动检测跌倒。声学 - FADE取得了令人鼓舞的成果：在实验室测试中，误报率为3%时检测率达到100%。在本文中，我们使用来自之前工作的数据集来研究如何进一步提高声学 - FADE的性能。为了分析跌倒与非跌倒信号之间的关系，我们使用改进的趋势视觉评估（iVAT）聚类算法结合基于最近邻的距离来找出最具挑战性的误报。然后，我们采用遗传算法（GA）框架进行特征选择，并找到能提高分类性能的梅尔频率倒谱系数（MFCC）。我们发现，仅使用三个MFCC系数（1、28、29）而非我们之前选择的（1、2、3、4、5、6）能提高分类性能。
