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.
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References
Bologna, M., Paparella, G., Fasano, A., Hallett, M., Berardelli, A.: Evolving concepts on bradykinesia. Brain 143(3), 727–750 (2019)
Lees, A.: Parkinson’s disease (2021)
Khan, T., Nyholm, D., Westin, J., Dougherty, M.: A computer vision framework for finger-tapping evaluation in Parkinson’s disease. Artif. Intell. Med. 60(1), 27–40 (2014)
Sano, Y., et al.: Quantifying Parkinson’s disease finger-tapping severity by extracting and synthesizing finger motion properties. Med. Biol. Eng. Comput. 54(6), 953–965 (2016). https://doi.org/10.1007/s11517-016-1467-z
Williams, S., et al.: The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J. Neurol. Sci. 416, 117003 (2020)
Williams, S., et al.: Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos. Artif. Intell. Med. 110, 101966 (2020)
HC, D.: An overview of you only look once: unified, real-time object detection. Int. J. Res. Appl. Sci. Eng. Technol. 8(6), 607–609 (2020)
Yi, Z., Yongliang, S., Jun, Z.: An improved tiny-yolov3 pedestrian detection algorithm. Optik 183, 17–23 (2019)
Goetz, C., et al.: Movement disorder society-sponsored revision of the unified parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. 23(15), 2129–2170 (2008)
Reeve, A., Simcox, E., Turnbull, D.: Ageing and Parkinson’s disease: why is advancing age the biggest risk factor? Ageing Res. Rev. 14, 19–30 (2014)
Magrinelli, F., et al.: Pathophysiology of motor dysfunction in parkinson’s disease as the rationale for drug treatment and rehabilitation. Parkinson’s Dis. 2016, 1–18 (2016)
Heldman, D., et al.: The modified bradykinesia rating scale for Parkinson’s disease: reliability and comparison with kinematic measures. Mov. Disord. 26(10), 1859–1863 (2011)
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Jaber, R., Qahwaji, R., Abdullatif, A., Buckley, J., Abd-Alhameed, R. (2022). Proposing a Three-Stage Model to Quantify Bradykinesia on a Symptom Severity Level Using Deep Learning. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_38
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DOI: https://doi.org/10.1007/978-3-030-87094-2_38
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