Parkinson's Disease (PD) is a degenerative disease of the central nervous system with a profound effect on the motor system. Symptoms include slowness of movement, rigidity of motion and in some patients, tremor. The severity of the disease is quantified using the Unified Parkinson Disease Rating Scale (UPDRS) which is a subjective scale performed and scored by physicians. In this work, we present an automated, objective quantitative analysis of four UPDRS motor examinations of Hand Movement and Finger Taps. For this purpose, a non-invasive system for recording and analysis of fine motor skills of hands was developed. The system is based on a simple low-cost depth acquisition sensor, similar to the second generation of Microsoft's Kinect sensor, and novel recursive self-correcting hand tracking algorithm. The system allows patients to perform test tasks in a natural and unhindered manner. The evaluation of the system was carried out on PD patients and controls. Machine Learning based classification was performed on the acquired data, followed by a decision making scheme.
|Title of host publication||2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 2014|
|Event||2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014 - Eilat, Israel|
Duration: 3 Dec 2014 → 5 Dec 2014
|Name||2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014|
|Conference||2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014|
|Period||3/12/14 → 5/12/14|
Bibliographical notePublisher Copyright:
© Copyright 2015 IEEE All rights reserved.
- Machine learning
- Parkinson's disease
- Support vector machine (SVM) classification
ASJC Scopus subject areas
- Electrical and Electronic Engineering