Neuromuscular incoordination in musician's dystonia

Oku, Tatsuo; Furuya, Shinichi

doi:10.1016/j.parkreldis.2019.05.011

Cited by 9 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the beginning, typical symptoms are subtle: feelings of tension in the forearm muscles when playing, slowing down of fast repetitive movements, and irregularity of movements required to be regular, e.g., scale playing in classical music. Later, curling of fingers or uncontrolled extension whilst playing, “sticking” fingers at the keys due to overactivity of finger-flexor muscles, and visible cramping of wrist extensors and flexors are more obvious ( 3 ), and can be observed objectively and assessed via video-rating of informed and trained raters ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Video-based diagnosis support system for pianists with Musician’s dystonia

Oku,

Furuya,

Lee

et al. 2024

Front. Neurol.

Self Cite

View full text Add to dashboard Cite

BackgroundMusician’s dystonia is a task-specific movement disorder that deteriorates fine motor control of skilled movements in musical performance. Although this disorder threatens professional careers, its diagnosis is challenging for clinicians who have no specialized knowledge of musical performance.ObjectivesTo support diagnostic evaluation, the present study proposes a novel approach using a machine learning-based algorithm to identify the symptomatic movements of Musician’s dystonia.MethodsWe propose an algorithm that identifies the dystonic movements using the anomaly detection method with an autoencoder trained with the hand kinematics of healthy pianists. A unique feature of the algorithm is that it requires only the video image of the hand, which can be derived by a commercially available camera. We also measured the hand biomechanical functions to assess the contribution of peripheral factors and improve the identification of dystonic symptoms.ResultsThe proposed algorithm successfully identified Musician’s dystonia with an accuracy and specificity of 90% based only on video footages of the hands. In addition, we identified the degradation of biomechanical functions involved in controlling multiple fingers, which is not specific to musical performance. By contrast, there were no dystonia-specific malfunctions of hand biomechanics, including the strength and agility of individual digits.ConclusionThese findings demonstrate the effectiveness of the present technique in aiding in the accurate diagnosis of Musician’s dystonia.

show abstract

Section: Introductionmentioning

confidence: 99%