Article

Diagnosis of people with mild Parkinson's symptoms is difficult. Nevertheless,
variations in gait pattern can be utilised to this purpose,
when measured via
Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree
of variability across individuals, and is subject to numerous nuisance factors.
Therefore, off-the-shelf Machine Learning techniques may fail to classify it with
the accuracy required in clinical trials. In this paper we propose a novel
framework in which IMU gait measurement sequences sampled during a 10m walk are
first encoded as hidden Markov models (HMMs) to extract their dynamics and
provide a fixed-length representation. Given sufficient training samples, the
distance between HMMs which optimises classification performance is learned and
employed in a classical Nearest Neighbour classifier. Our tests demonstrate how
this technique achieves accuracy of 85.51% over a 156 people with Parkinson's
with a representative range of severity and 424 typically developed adults, which
is the top performance achieved so far over a cohort of such size, based on
single measurement outcomes. The method displays the potential for further
improvement and a wider application to distinguish other conditions.
CI - Copyright (c) 2017 Elsevier B.V. All rights reserved.

Langue : ANGLAIS