Department of Physical Medicine and Rehabilitation, China Medical University Hospital, 40402 Taichung, Taiwan.
Department of Traditional Chinese Medicine, Asia University Hospital, 41354 Taichung, Taiwan.
J Healthc Eng. 2021 Nov 25;2021:6252445. doi: 10.1155/2021/6252445. eCollection 2021.
Falls put older adults at great risk and are related to the body's sense of balance. This study investigated how to detect the possibility of high fall risk subjects among older adults. The original signal is based on center of pressure (COP) measured using a force plate. The falling group includes 29 subjects who had a history of falls in the year preceding this study or had received high scores on the Short Falls Efficacy Scale (FES). The nonfalling group includes 47 enrollees with no history of falls and who had received low scores on the Short FES. The COP in both the anterior-posterior and mediolateral direction were calculated and analyzed through empirical mode decomposition (EMD) up to six levels. The following five features were extracted and imported to a decision tree algorithm: root-mean-square deviation, median frequency, total frequency power, approximate entropy, and sample entropy. The results showed that there were a larger number of statistically different feature parameters, and a higher classification of accuracy was obtained. With the aid of empirical mode decomposition, the average classification accuracy increased 10% and achieved a level of 99.74% in the training group and 96.77% in the testing group, respectively.
老年人跌倒的风险很大，这与身体的平衡感有关。本研究旨在探讨如何检测老年人中高跌倒风险人群的可能性。原始信号基于使用测力板测量的中心压力（COP）。跌倒组包括 29 名在本研究前一年有跌倒史或在短跌倒效能量表（FES）中得分较高的受试者。非跌倒组包括 47 名无跌倒史且在短 FES 中得分较低的受试者。对前后方向和左右方向的 COP 进行计算，并通过经验模态分解（EMD）进行了六级分析。提取并导入决策树算法的五个特征包括：均方根偏差、中值频率、总频率功率、近似熵和样本熵。结果表明，有更多具有统计学差异的特征参数，并且获得了更高的分类准确性。借助经验模态分解，平均分类准确性在训练组中提高了 10%，达到了 99.74%，在测试组中达到了 96.77%。
