No / 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.

No / 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.

Rigidity is one of the cardinal symptoms in Parkinson's disease, along with Bradykinesia, tremor and postural instability. Rigidity in PD has been understudied, but its pathophysiological basis remains unclear. Various types of neurophysiological and biomechanical approach have been developed in order to investigate the neural control of muscle tone. A common approach is to observe the sensitivity of muscle resistance in response to stretch velocity or displacement [Kamper, Rea, He]. A recent study on elbow flexors in patients with spasticity and rigidity showed a velocity dependent increase in reactive torque in both groups [Lee H, et al). Even though this Study shows a correlation between elbow flexors and velocity, it doesn't discuss the role of elbow extensors. We studied the rigidity response in the elbow of both arms to different speed movements in 12 patients suffering from Parkinson's disease ON or OFF medication. The purpose of this study was to look at both elbow flexion and extension and show that quantitative measures of rigidity and movement disorders in subjects with Parkinson's disease correlate with the currently used clinical evaluations and also find the correlation between velocity and both elbow extension and flexion at the same time. Elbow was flexed and extended by means of a robotic arm,under four different speeds. The resistance to movement was recorded with a torque sensor and EMG of two elbow muscles; Biceps and Triceps; was recorded while the subjects were attempting to relax. The patients were also examined by physicians and their elbow rigidity and muscle tone and Parkinson's disease stage was evaluated and a Universal score in the categories of UPDRS, MMSE, and CAPIT was assigned for each arm of each individual. In the end we will argue that there is a very strong correlation between speed and elbow Extension and Flexion, muscle activity and the rigidity presented in each arm. We will also present the correlation between the robotic torque measurement and the clinical scores given to each subject.

Parkinson’s disease causes among other symptoms also writing disorder. Parkinson's dysgrafia is disease the writing of parkinsonics. The aim of the work is to show the importance of examinig the parametres of Parkinson's dysgrafia and to find writing parametres, which could distinguish healthy subjects from the pacient and also it could monitoring progress of pakinson's disease. Some of the parametrs showed marked differences and therefore could distinguish healthy people from those with Parkinson’s disease.

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