Objective Evaluation of Cervical Dystonia Using an Inertial Sensor-Based System View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-04-09

AUTHORS

Jonghyun Park, Kyung Yong Yang, Joonnyong Lee, Kibum Youn, Jehee Lee, Sun Gun Chung, Hee Chan Kim, Keewon Kim

ABSTRACT

Cervical dystonia (CD) is a chronic neurological movement disorder that causes involuntary neck muscle contractions, leading to abnormal movements or posture of the head. However, objective evaluation of severity of CD is lacking in the clinical practice. This study aimed to provide an objective evaluation of the severity of CD based on three-dimensional (3D) kinematics of the neck using wearable inertial sensors. Each inertial sensor was attached to the anterior chest and head of eight patients with idiopathic CD. The involuntary movements of neck were quantified by the rotation angle (RA) and magnitude of angular velocity (MAV) through axis-angle representation expressing 3D rotation. The characteristic posture of each patient was visualized. Four kinematic parameters, the mean and peak values of RA and MAV (Meanangle, Peakangle, Meanvel, and Peakvel) were calculated and correlation between each parameter values and the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) scores from all patients was analyzed to assess clinical validity. The test–retest reliability of the parameters was examined as well. Correlation coefficients of the Meanangle and Peakangle with TWSTRS were 0.28 and 0.69, respectively, whereas those of the Meanvel and Peakvel were 0.84 and 0.84, respectively. The MAV parameters showed a higher correlation with clinical severity than RA parameters. The intraclass correlation coefficients of the four parameters between the test–retest were more than 0.9. We conclude that objective evaluation of severity of CD based on kinematic parameters is clinically valid and has test–retest reliability. More... »

PAGES

1-10

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http://scigraph.springernature.com/pub.10.1007/s40846-018-0400-3

DOI

http://dx.doi.org/10.1007/s40846-018-0400-3

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https://app.dimensions.ai/details/publication/pub.1103198130


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