The potential of computer games peripherals to measure the motor dysfunction in Parkinson's diseases is assessed. Of particular interest is the quantification of bradykinesia. Previous studies used modified or custom haptic interfaces, here an unmodified force feedback joystick and steering wheel are used with a laptop. During testing an on screen cursor moves in response to movements of the peripheral, the user has to track a continuously moving target (pursuit tracking), or move to a predetermined target (step tracking). All tasks use movement in the horizontal axis, allowing use of joystick or steering wheel. Two pursuit tracking tasks are evaluated, pseudo random movement, and a swept frequency task. Two step tracking tasks are evaluated, movement between two or between two of five fixed targets. Thirteen patients and five controls took part on a weekly basis. Patients were assessed for bradykinesia at each session using standard clinical measures. A range of quantitative measures was developed to allow comparison between and within patients and controls using analysis of variance (ANOVA). Both peripherals are capable of discriminating between controls and patients, and between patients with different levels of bradykinesia. Recommendations for test procedures and peripherals are given.
Parkinson's disease (PD) is a common neurodegenerative disease and the diagnosis of its idiopathic form remains challenging. The diagnosis of idiopathic form is based on clinical features which can have poor sensitivity with about 25% of patients diagnosed as having the disease actually having other conditions. In this study we assess the suitability and clinical value of a low cost computer-based system as an aid to diagnosis of PD, in particular the presence of tremor. All participants (12 patients and 10 controls) performed a shape-tracing task using a graphic tablet attached to a laptop. To assess the presence of tremors in the collected data, a statistical spectral analysis of the moment-to-moment fluctuations in the position signal of the output from the digitising tablet was performed. This allowed the comparison of power spectrums obtained from the control and patient responses respectively. A peak in log power between the 5 Hz & 6 Hz can clearly be identified in the patient's spectrum and is indicative of Parkinson's related tremor and no similar peak could be seen in the control's spectrum, suggesting this type of sequential task and automated data analysis may be useful in the diagnosis of tremor.
This paper describes the novel application of an evolutionary algorithm to discriminate Parkinson's patients from age-matched controls in their response to simple figure-copying tasks. The reliable diagnosis of Parkinson's disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approach described in this paper aims to distinguish between the velocity profiles of pen movements of patients and controls to identify distinguishing artifacts that may be indicative of the Parkinson's symptom bradykinesia. Results are presented for 12 patients with Parkinson's disease and 10 age-match controls. An algorithm was evolved using half the patient and age-matched control responses, which was then successfully used to correctly classify the remaining responses. A more rigorous ''leave one out'' strategy was also applied to the test data with encouraging results.
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