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As the world ages, elderly care becomes a big concern of the society. Toaddress the elderly's issues on dementia and fall risk, we have investigatedsmart cognitive and fall risk assessment with machine learning methodologybased on the data collected from finger tapping test and Timed Up and Go (TUG)test. Meanwhile, we have discovered the associations between cognition andfinger motion from finger tapping data and the association between fall riskand gait characteristics from TUG data. In this paper, we jointly analyze thefinger tapping and gait characteristics data with copula entropy. We find thatthe associations between certain finger tapping characteristics ('number oftaps', 'average interval of tapping', 'frequency of tapping' of both hands ofbimanual inphase and those of left hand of bimanual untiphase) and TUG scoreare relatively high. According to this finding, we propose to utilize thisassociations to improve the predictive models of automatic fall risk assessmentwe developed previously. Experimental results show that using thecharacteristics of both finger tapping and gait as inputs of the predictivemodels of predicting TUG score can considerably improve the predictionperformance in terms of MAE compared with using only one type ofcharacteristics.

Associations between finger tapping, gait and fall risk with application to fall risk assessment