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Proposing a Three-Stage Model to Quantify Bradykinesia on a Symptom Severity Level Using Deep Learning by R. Jaber; Rami Qahwaji; Amr Abdullatif; J. Buckley; R. Abd-Alhameed is a book available to read on EtoBox.

What is Proposing a Three-Stage Model to Quantify Bradykinesia on a Symptom Severity Level Using Deep Learning about?

Typically characterised as a movement disorder, bradykinesia can be represented according to the degree of motor impairment. The assessment criteria for Parkinson's disease (PD) is therefore well defined due to its symptomatic nature. Diagnosing and monitoring the progression of bradykinesia is currently heavily reliant on clinician's visual judgment. One of the most common forms of examining bradykinesia involves rapid finger tapping and is aimed to determine the patient's ability to initiate and sustain movement effectively. This consists of the patient repeatedly tapping their index finger and thumb together. Object detection algorithm, YOLO, was trained to track the separation between the index finger and thumb. Bounding boxes (BB) were used to determine their relative position on a frame-to-frame basis to produce a time series signal. Key movement characteristics were extracted to determine regularity of movement in finger tapping amongst Parkinson's patients and controls.

Author

R. Jaber; Rami Qahwaji; Amr Abdullatif; J. Buckley; R. Abd-Alhameed

Publisher

Springer International Publishing : Imprint: Springer

Published

2021

Language

EN

ISBN

9783030870935

Subjects

Engineering, Computer Science, Science