  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.
