3 research outputs found
Predicting TUG score from gait characteristics with video analysis and machine learning
Fall is a leading cause of death which suffers the elderly and society. Timed
Up and Go (TUG) test is a common tool for fall risk assessment. In this paper,
we propose a method for predicting TUG score from gait characteristics
extracted from video with computer vision and machine learning technologies.
First, 3D pose is estimated from video captured with 2D and 3D cameras during
human motion and then a group of gait characteristics are computed from 3D pose
series. After that, copula entropy is used to select those characteristics
which are mostly associated with TUG score. Finally, the selected
characteristics are fed into the predictive models to predict TUG score.
Experiments on real world data demonstrated the effectiveness of the proposed
method. As a byproduct, the associations between TUG score and several gait
characteristics are discovered, which laid the scientific foundation of the
proposed method and make the predictive models such built interpretable to
clinical users.Comment: Experimental results and discussion are revised. The code for
estimating copula entropy is available at https://github.com/majianthu/copen
Associations between finger tapping, gait and fall risk with application to fall risk assessment
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
Predicting MMSE Score from Finger-Tapping Measurement
Dementia is a leading cause of diseases for the elderly. Early diagnosis is
very important for the elderly living with dementias. In this paper, we propose
a method for dementia diagnosis by predicting MMSE score from finger-tapping
measurement with machine learning pipeline. Based on measurement of finger
tapping movement, the pipeline is first to select finger-tapping attributes
with copula entropy and then to predict MMSE score from the selected attributes
with predictive models. Experiments on real world data show that the predictive
models such developed present good prediction performance. As a byproduct, the
associations between certain finger-tapping attributes ('Number of taps',
'Average of intervals', and 'Frequency of taps' of both hands of bimanual
in-phase task) and MMSE score are discovered with copula entropy, which may be
interpreted as the biological relationship between cognitive ability and motor
ability and therefore makes the predictive models explainable. The selected
finger-tapping attributes can be considered as dementia biomarkers.Comment: 11 pages, 4 figures, 2 table