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

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    Publication date
    2021-11-18
    Author
    Jaber, R.
    Qahwaji, Rami S.R.
    Buckley, John G.
    Abd-Alhameed, Raed A.
    Keyword
    Bradykinesia
    Deep learning
    Peer-Reviewed
    Yes
    Open Access status
    closedAccess
    
    Metadata
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    Abstract
    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.
    URI
    http://hdl.handle.net/10454/18822
    Version
    No full-text in the repository
    Citation
    Jaber R, Qahwaji RSR and Buckley JG et al (2021) Proposing a Three-Stage Model to Quantify Bradykinesia on a Symptom Severity Level Using Deep Learning. In: Jansen T, Jensen R, Mac Parthalain N et al (Eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing. Springer, Cham. 1409: 429-438.
    Link to publisher’s version
    https://doi.org/10.1007/978-3-030-87094-2_38
    Type
    Conference paper
    Collections
    Engineering and Informatics Publications

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