An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters

Tiwana Varrecchia, Stefano Filippo Castiglia, Alberto Ranavolo, Carmela Conte, Antonella Tatarelli, Gianluca Coppola, Cherubino Di Lorenzo, Francesco Draicchio, Francesco Pierelli, Mariano Serrao

Research output: Contribution to journalArticlepeer-review


Introduction Gait deficits are debilitating in people with Parkinson's disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical features and progression of the disease. Objectives Our study aimed to i) develop an automated diagnostic algorithm based on machine-learning techniques (artificial neural networks [ANNs]) to classify the gait deficits of PwPD according to disease progression in the Hoehn and Yahr (H-Y) staging system, and ii) identify a minimum set of gait classifiers. Methods We evaluated 76 PwPD (H-Y stage 1-4) and 67 healthy controls (HCs) by computerized gait analysis. We computed the time-distance parameters and the ranges of angular motion (RoMs) of the hip, knee, ankle, trunk, and pelvis. Principal component analysis was used to define a subset of features including all gait variables. An ANN approach was used to identify gait deficits according to the H-Y stage. Results We identified a combination of a small number of features that distinguished PwPDs from HCs (one combination of two features: knee and trunk rotation RoMs) and identified the gait patterns between different H-Y stages (two combinations of four features: walking speed and hip, knee, and ankle RoMs; walking speed and hip, knee, and trunk rotation RoMs). Conclusion The ANN approach enabled automated diagnosis of gait deficits in several symptomatic stages of Parkinson's disease. These results will inspire future studies to test the utility of gait classifiers for the evaluation of treatments that could modify disease progression.

Original languageEnglish
Article numbere0244396
JournalPLoS One
Issue number2 February
Publication statusPublished - Feb 2021

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General


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