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

      Jaber, R.; Qahwaji, Rami S.R.; Buckley, John G.; Abd-Alhameed, Raed A. (Springer International Publishing, 2021-11-18)
      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.
    • Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos

      Williams, S.; Relton, S.D.; Fang, H.; Alty, J.; Qahwaji, Rami S.R.; Graham, C.D.; Wong, D.C. (2020-11)
      Background: Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. Aim: We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia. Methods: We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0–1) or mild/moderate/severe bradykinesia (UPDRS = 2–4), and presence or absence of Parkinson's diagnosis. Results: A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. Conclusion: The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.