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A computer vision framework for finger-tapping evaluation in Parkinson's disease
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Uppsala University.
Högskolan Dalarna.
Högskolan Dalarna.
2014 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 60, no 1, 27-40 p.Article in journal (Refereed) Published
Abstract [en]

Objectives: The rapid finger-tapping test (RFT) is an important method for clinical evaluation of movement disorders, including Parkinson's disease (PD). In clinical practice, the naked-eye evaluation of RFT results in a coarse judgment of symptom scores. We introduce a novel computer-vision (CV) method for quantification of tapping symptoms through motion analysis of index-fingers. The method is unique as it utilizes facial features to calibrate tapping amplitude for normalization of distance variation between the camera and subject.

Methods: The study involved 387 video footages of RFT recorded from 13 patients diagnosed with advanced PD. Tapping performance in these videos was rated by two clinicians between the symptom severity levels (‘0: normal’ to ‘3: severe’) using the unified Parkinson's disease rating scale motor examination of finger-tapping (UPDRS-FT). Another set of recordings in this study consisted of 84 videos of RFT recorded from 6 healthy controls. These videos were processed by a CV algorithm that tracks the index-finger motion between the video-frames to produce a tapping time-series. Different features were computed from this time series to estimate speed, amplitude, rhythm and fatigue in tapping. The features were trained in a support vector machine (1) to categorize the patient group between UPDRS-FT symptom severity levels, and (2) to discriminate between PD patients and healthy controls.

Results: A new representative feature of tapping rhythm, ‘cross-correlation between the normalized peaks’ showed strong Guttman correlation (mu2 = -0.80) with the clinical ratings. The classification of tapping features using the support vector machine classifier and 10-fold cross validation categorized the patient samples between UPDRS-FT levels with an accuracy of 88%. The same classification scheme discriminated between RFT samples of healthy controls and PD patients with an accuracy of 95%.

Conclusion: The work supports the feasibility of the approach, which is presumed suitable for PD monitoring in the home environment. The system offers advantages over other technologies (e.g. magnetic sensors, accelerometers, etc.) previously developed for objective assessment of tapping symptoms.

Place, publisher, year, edition, pages
Elsevier , 2014. Vol. 60, no 1, 27-40 p.
Keyword [en]
Computer vision, Motion analysis, Face detection, Parkinson's disease, Finger-tapping
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-23608DOI: 10.1016/j.artmed.2013.11.004ISI: 000331506200003Scopus ID: 2-s2.0-84892883460OAI: oai:DiVA.org:mdh-23608DiVA: diva2:679572
Projects
E-MOTIONS
Available from: 2013-12-16 Created: 2013-12-16 Last updated: 2015-07-15Bibliographically approved
In thesis
1. First-principle data-driven models for assessment of motor disorders in Parkinson’s disease
Open this publication in new window or tab >>First-principle data-driven models for assessment of motor disorders in Parkinson’s disease
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic. 

The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait.

The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Place, publisher, year, edition, pages
Sweden: Mälardalen University, 2014. 102 p.
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 153
National Category
Engineering and Technology
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-24647 (URN)978-91-7485-142-7 (ISBN)
Public defence
2014-04-16, Clas Ohlson, Studenternas Hus Tenoren, Campus Borlänge, 13:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2014-03-17 Created: 2014-03-14 Last updated: 2015-07-15Bibliographically approved

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