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|Title: ||Objective evaluation of bradykinesia in Parkinson’s disease using an inexpensive marker-less motion tracking system|
|Authors: ||Lih, Wee-Lih|
|Issue Date: ||Jan-2019|
|Publisher: ||IOP Publishing Ltd|
|Citation: ||Lee, W. L., N. C. Sinclair, M. Jones, J. L. Tan, E. L. Proud, R. Peppard, H. J. McDermott, and T. Perera. 2019. Objective evaluation of bradykinesia in Parkinson's disease using an inexpensive marker-less motion tracking system. Physiological Measurement. 40(1): 014004.|
|Abstract: ||OBJECTIVE: Quantification of bradykinesia (slowness of movement) is crucial for the treatment and monitoring of Parkinson's disease. Subjective observational techniques are the de-facto 'gold standard', but such clinical rating scales suffer from poor sensitivity and inter-rater variability. Although various technologies have been developed for assessing bradykinesia in recent years, most still require considerable expertise and effort to operate. Here we present a novel method to utilize an inexpensive off-the-shelf hand-tracker (Leap Motion) to quantify bradykinesia. Approach: Eight participants with Parkinson's disease receiving benefit from deep brain stimulation were recruited for the study. Participants were assessed "on" and "off" stimulation, with the "on" condition repeated to evaluate reliability. Participants performed wrist pronation/supination, hand open/close, and finger-tapping tasks during each condition. Tasks were simultaneously captured by our software and rated by three clinicians. A linear regression model was developed to predict clinical scores and its performance was assessed with leave-one-out cross validation. Main Results: Aggregate bradykinesia scores predicted by our method were in strong agreement (R = 0.86) with clinical scores. The model was able to differentiate therapeutic states and comparison between the test-retest conditions yielded no significant difference (p = 0.50). Significance: These findings demonstrate that our method can objectively quantify bradykinesia in agreement with clinical observation and provide reliable measurements over time. The hardware is readily accessible, requiring only a modest computer and our software to perform assessments, thus making it suitable for both clinic- and home-based symptom tracking.
|Appears in Collections:||Neurobionics Research Publications|
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