High Accuracy Discrimination of Parkinson’s Disease from Healthy Controls by Hand Movements Analysis Using LeapMotion Sensor and 1D Convolutional Neural Network

This study is devoted to the detection of hand motor impairment in Parkinson’s disease (PD) using ID convolutional neural network (CNN). The data were obtained from the LeapMotion sensor when performing hand motor tasks in the group of patients with PD and the control group. CNN consists of2 blocks: three parallel ID convolution blocks and a common fully connected block. CNN was trained on a data set of each hand fo r three motor tasks: finger tapping, finger opening-closing, pronation-supination of the hands. Binary classification (PD veisus non-PD) was performed be the CNN itself, as well as by several conventional classifieis (knearest neighbors, SVM, Decision Tree, and Random Forest) that were used instead of the output neuron of fully connected block of the CNN. Additionally, the best feature set was selected using logistic regression. The testing was conducted in the 8-fold cross-validation mode; the best obtained accuracy of the correct classification was S5.1 % using CNN with the SVM classifier.