UPDRS Tests for Diagnosis of Parkinson's Disease Employing Virtual-Touchpad

Kupryjanow, Adam; Kunka, Bartosz; Kostek, Bożena

doi:10.1109/dexa.2010.87

Cited by 21 publications

(8 citation statements)

References 8 publications

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“…Instead of relying on the subjective assessment from neurologists, four SVM classifiers were trained separately for every hand gesture in another study. 17 Every classifier returns the decision (gesture belongs to the given class or not) and the probability of this decision with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Biomechanical parameter assessment for classification of Parkinson’s disease on clinical scale

Butt

Rovini

Esposito

et al. 2017

International Journal of Distributed Sensor Networks

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The primary goal of this study was to investigate computerized assessment methods to classify motor dysfunctioning of patients with Parkinson's disease on the clinical scale. In this proposed system, machine learning-based computerized assessment methods were introduced to assess the motor performance of patients with Parkinson's disease. Biomechanical parameters were acquired from six exercises through wearable inertial sensors: SensFoot V2 and SensHand V1. All patients were evaluated via neurologist by means of the clinical scale. The average rating was calculated from all exercise ratings given by clinicians to estimate overall rating for each patient. Patients were divided in two groups: slight-mild patients with Parkinson's disease and moderate-severe patients with Parkinson's disease according to average rating (''0: slight and mild'' and ''1: moderate and severe''). Feature selection methods were used for the selection of significant features. Selected features were trained in support vector machine, logistic regression, and neural network to classify the two groups of patients. The highest classification accuracy obtained by support vector machine classifier was 79.66%, with 0.8790 area under the curve. A 76.2% classification accuracy was obtained with 0.7832 area under the curve through logistic regression. A 83.10% classification accuracy was obtained by neural network classifier, with 0.889 area under the curve. Strong distinguishability of the models between the two groups directs the high possibility of motor impairment classification through biomechanical parameters in patients with Parkinson's disease based on the clinical scale.

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Section: Introductionmentioning

confidence: 99%

Biomechanical parameter assessment for classification of Parkinson’s disease on clinical scale

Butt

Rovini

Esposito

et al. 2017

International Journal of Distributed Sensor Networks

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“…In terms of diagnosis Kupryjanow et al [35] came up with an alternative measurement technique for determining UPDRS sub-scores related to motor tests (i.e. finger tapping and rapid alternating movement of hands).…”

Section: Othermentioning

confidence: 99%

“…The succession of those postures is used to extract the mentioned features and determine UPDRS scores. The authors did not describe a user study in [35].…”

Section: Othermentioning

confidence: 99%

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2013

HIIJ

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“…These have focused on methods that use wearable devices and sensors such as wireless accelerometers [4], wearable triaxial gyroscopes [5], virtual touchpads [6], [7] or EOG signal analysis (i.e. analysis of the electric signals originating in the eye) [8].…”

Section: Introductionmentioning

confidence: 99%

Automatic assessment of Parkinson's Disease from natural hands movements using 3D depth sensor

Dror

Yanai

Peleg

et al. 2014

2014 IEEE 28th Convention of Electrical &Amp; Electronics Engineers in Israel (IEEEI)

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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.

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UPDRS Tests for Diagnosis of Parkinson's Disease Employing Virtual-Touchpad

Cited by 21 publications

References 8 publications

Biomechanical parameter assessment for classification of Parkinson’s disease on clinical scale

Biomechanical parameter assessment for classification of Parkinson’s disease on clinical scale

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Automatic assessment of Parkinson's Disease from natural hands movements using 3D depth sensor

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