scholarly journals Computer keyboard interaction as an indicator of early Parkinson’s disease

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
L. Giancardo ◽  
A. Sánchez-Ferro ◽  
T. Arroyo-Gallego ◽  
I. Butterworth ◽  
C. S. Mendoza ◽  
...  

Abstract Parkinson’s disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
L. Giancardo ◽  
A. Sánchez-Ferro ◽  
T. Arroyo-Gallego ◽  
I. Butterworth ◽  
C. S. Mendoza ◽  
...  

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
João W. M. de Souza ◽  
Shara S. A. Alves ◽  
Elizângela de S. Rebouças ◽  
Jefferson S. Almeida ◽  
Pedro P. Rebouças Filho

Parkinson’s disease affects millions of people around the world and consequently various approaches have emerged to help diagnose this disease, among which we can highlight handwriting exams. Extracting features from handwriting exams is an important contribution of the computational field for the diagnosis of this disease. In this paper, we propose an approach that measures the similarity between the exam template and the handwritten trace of the patient following the exam template. This similarity was measured using the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template. The proposed approach was evaluated using various exam templates and the handwritten traces of the patient. Each of these variations was used together with the Naïve Bayes, OPF, and SVM classifiers. In conclusion the proposed approach was proven to be better than the existing methods found in the literature and is therefore a promising tool for the diagnosis of Parkinson’s disease.


Sign in / Sign up

Export Citation Format

Share Document