Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: HighlightsHandwriting movements can be used to assist in the diagnosis of Parkinson's disease.The pressure exerted on the surface during handwriting contributes to the diagnosis of PD.A handwriting database containing samples from 75 PD and healthy subjects is presented. Objective present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. Methods and materialThe database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting,  investigated new pressure features based on the pressure exerted on the writing surface. To discriminate beten PD patients and healthy subjects, three different classifiers re compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). ResultsFor predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features. ConclusionExperimental results shod that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate beten PD patients and healthy controls.
['Peter Drotár', 'Jiří Mekyska', 'Irena Rektorová', 'Lucia Masarová', 'Zdeněk Smékal', 'Marcos Faundez-Zanuy']
I am writing a research paper on a idea Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease
[{"from": "human", "value": "I am writing a research paper on a idea Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: HighlightsHandwriting movements can be used to assist in the diagnosis of Parkinson's disease.The pressure exerted on the surface during handwriting contributes to the diagnosis of PD.A handwriting database containing samples from 75 PD and healthy subjects is presented. Objective present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. Methods and materialThe database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting,  investigated new pressure features based on the pressure exerted on the writing surface. To discriminate beten PD patients and healthy subjects, three different classifiers re compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). ResultsFor predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features. ConclusionExperimental results shod that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate beten PD patients and healthy controls."}]