An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors

Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wear...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-06, Vol.19 (11), p.2644
Main Authors: Bobić, Vladislava, Djurić-Jovičić, Milica, Dragašević, Nataša, Popović, Mirjana B, Kostić, Vladimir S, Kvaščev, Goran
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
atypical parkinsonism
automatic scoring
Biosensing Techniques
Brain diseases
Clinical medicine
decision support system
Electrical engineering
Expert systems
finger-tapping
Fingers - physiology
Humans
Hypokinesia - physiopathology
Motion perception
Movement - physiology
Parkinson's disease
Principal components analysis
Researchers
Sensors
Signal processing
Signs and symptoms
UPDRS
Wearable Electronic Devices
wearable inertial sensors
Wearable technology
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Summary:Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson's disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19112644