Liu Ying, Redmond Stephen J, Narayanan Michael R, Lovell Nigel H
Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1499-502. doi: 10.1109/IEMBS.2011.6090342.
Falls are a prominent problem facing older adults and a common cause of hospitalized injuries. Accurate falls-risk assessment and classification of falls-risk levels will provide useful information for the prevention of future falls. This study presents a triaxial accelerometer (TA) based two-class classifier, which discriminates between multiple fallers and non-multiple fallers, using a directed-routine (DR) movement test. One-hundred-and-twenty-six features were extracted from the accelerometry signals, recorded during the DR tests using a waist mounted TA, from 68 subjects. A linear multiple regression model was employed to map a subset of these features to an estimate of the number of previous falls experienced in the preceding twelve months. A simple threshold is applied to this estimated number of falls to create a basic linear discriminant classifier to separate multiple from non-multiple fallers. The system attained an accuracy of 71% in classifying the exact number of falls experienced in the last 12 months and 97% in identifying multiple fallers.
跌倒是老年人面临的一个突出问题，也是住院受伤的常见原因。准确的跌倒风险评估和跌倒风险水平分类将为预防未来跌倒提供有用信息。本研究提出了一种基于三轴加速度计（TA）的二类分类器，该分类器使用定向日常（DR）运动测试来区分多次跌倒者和非多次跌倒者。从68名受试者使用腰部佩戴的TA在DR测试期间记录的加速度信号中提取了126个特征。采用线性多元回归模型将这些特征的一个子集映射到对前十二个月经历的跌倒次数的估计。对这个估计的跌倒次数应用一个简单的阈值，以创建一个基本的线性判别分类器，将多次跌倒者与非多次跌倒者区分开来。该系统在对过去12个月经历的跌倒确切次数进行分类时准确率达到71%，在识别多次跌倒者方面准确率达到97%。
