  Parkinson's Disease (PD) is a chronic and progressive neurological disorder
that results in rigidity, tremors and postural instability. There is no
definite medical test to diagnose PD and diagnosis is mostly a clinical
exercise. Although guidelines exist, about 10-30% of the patients are wrongly
diagnosed with PD. Hence, there is a need for an accurate, unbiased and fast
method for diagnosis. In this study, we propose LPGNet, a fast and accurate
method to diagnose PD from gait. LPGNet uses Linear Prediction Residuals (LPR)
to extract discriminating patterns from gait recordings and then uses a 1D
convolution neural network with depth-wise separable convolutions to perform
diagnosis. LPGNet achieves an AUC of 0.91 with a 21 times speedup and about 99%
lesser parameters in the model compared to the state of the art. We also
undertake an analysis of various cross-validation strategies used in literature
in PD diagnosis from gait and find that most methods are affected by some form
of data leakage between various folds which leads to unnecessarily large models
and inflated performance due to overfitting. The analysis clears the path for
future works in correctly evaluating their methods.
