Gait Anomaly Detection of Subjects with Parkinson?s Disease Using a Deep Time Series-based Approach

Parkinson's disease (PD) is a cognitive degenerative
disorder of the central nervous system that mainly affects the
motor system. The earliest symptoms evidence a general deficit
of coordination and an unsteady gait.
Current approaches for the evaluation and assessment of gait
disturbances in PD have proved to be expensive, inconvenient
and ineffective in the detection of anomalous walking patterns.
In this paper, we address these issues by defining a deep time
series-based approach for the detection of anomalous walking
patterns in the gait dynamics of elderly people by analyzing the
acceleration values of their movements.
The results show a training accuracy and testing accuracy of over
90% with an accuracy improvement of 4.28% in comparison with
related works.