A novel approach in combination of 3D gait analysis data for aiding clinical decision-making in patients with Parkinson’s disease

The most common methods used by neurologist to evaluate Parkinson’s Disease (PD) patients are rating scales, that are affected by subjective and non-repeatable observations. Since several research studies have revealed that walking is a sensitive indicator for the progression of PD. In this paper, we propose an innovative set of features derived from three-dimensional Gait Analysis in order to classify motor signs of motor impairment in PD and differentiate PD patients from healthy subjects or patients suffering from other neurological diseases. We consider kinematic data from Gait Analysis as Gait Variables Score (GVS), Gait Profile Score (GPS) and spatio-temporal data for all enrolled patients. We then carry out experiments evaluating the extracted features using an Artificial Neural Network (ANN) classifier. The obtained results are promising with the best classifier score accuracy equal to 95.05%.