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Motion Cue Analysis for Parkinsonian Gait Recognition
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Dalarna University.
Dalarna University.
Dalarna University.
2013 (Engelska)Ingår i: Open Biomedical Engineering Journal, ISSN 1874-1207, E-ISSN 1874-1207, Vol. 7, nr 1, s. 1-8Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson’s disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject’s body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

Ort, förlag, år, upplaga, sidor
Netherlands: Bentham Science , 2013. Vol. 7, nr 1, s. 1-8
Nyckelord [en]
Gait impairment, Parkinson’s disease, Gait video analysis, Image processing.
Nationell ämneskategori
Teknik och teknologier
Identifikatorer
URN: urn:nbn:se:mdh:diva-23612DOI: 10.2174/1874120701307010001Scopus ID: 2-s2.0-84876092108OAI: oai:DiVA.org:mdh-23612DiVA, id: diva2:679483
Projekt
E-MOTIONSTillgänglig från: 2013-12-16 Skapad: 2013-12-16 Senast uppdaterad: 2017-12-06Bibliografiskt granskad
Ingår i avhandling
1. First-principle data-driven models for assessment of motor disorders in Parkinson’s disease
Öppna denna publikation i ny flik eller fönster >>First-principle data-driven models for assessment of motor disorders in Parkinson’s disease
2014 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Sweden: Mälardalen University, 2014. s. 102
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 153
Nationell ämneskategori
Teknik och teknologier
Forskningsämne
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-24647 (URN)978-91-7485-142-7 (ISBN)
Disputation
2014-04-16, Clas Ohlson, Studenternas Hus Tenoren, Campus Borlänge, 13:00 (Engelska)
Opponent
Handledare
Forskningsfinansiär
KK-stiftelsen
Tillgänglig från: 2014-03-17 Skapad: 2014-03-14 Senast uppdaterad: 2015-07-15Bibliografiskt granskad

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