论文标题
手指窃听，步态和跌倒风险与跌倒风险评估之间的关联
Associations between finger tapping, gait and fall risk with application to fall risk assessment
论文作者
论文摘要
随着世界的年龄，老年护理成为社会的主要关注点。为了解决老年人在痴呆症和跌倒风险方面的问题，我们根据从手指敲击测试中收集的数据并进行时间安排和GO（TUG）测试，通过机器学习方法研究了智能认知和跌倒风险评估。同时，我们发现了手指攻击数据的认知和手指运动之间的关联，以及拖离数据的跌落风险与步态特征之间的关联。在本文中，我们用副熵共同分析了手指的攻击和步态特征数据。我们发现，某些手指挖掘特性（“抽头的数量”，“敲击的平均间隔”，“敲击的平均间隔”，“敲击的频率”双手插入的手的频率和双手脉冲的左手）和TUG分数相对较高。根据这一发现，我们建议利用这种关联来改善我们先前开发的自动秋季风险评估的预测模型。实验结果表明，将手指敲击和步态的特征作为预测拖线得分的预测模型的输入可以大大改善MAE的预测性能，而仅使用一种类型的特征。
As the world ages, elderly care becomes a big concern of the society. To address the elderly's issues on dementia and fall risk, we have investigated smart cognitive and fall risk assessment with machine learning methodology based on the data collected from finger tapping test and Timed Up and Go (TUG) test. Meanwhile, we have discovered the associations between cognition and finger motion from finger tapping data and the association between fall risk and gait characteristics from TUG data. In this paper, we jointly analyze the finger tapping and gait characteristics data with copula entropy. We find that the associations between certain finger tapping characteristics ('number of taps', 'average interval of tapping', 'frequency of tapping' of both hands of bimanual inphase and those of left hand of bimanual untiphase) and TUG score are relatively high. According to this finding, we propose to utilize this associations to improve the predictive models of automatic fall risk assessment we developed previously. Experimental results show that using the characteristics of both finger tapping and gait as inputs of the predictive models of predicting TUG score can considerably improve the prediction performance in terms of MAE compared with using only one type of characteristics.
