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Our study suggests that a sensor-based technique might be an important adjunct to existing clinical measures to improve the management of patients undergoing Parkinson's control therapy via deep-brain stimulation. In the future, clinicians could gather accelerometer data for approximately one week before and after outpatient visits for deep-brain stimulation adjustments. An expert system could then...
Parkinson's disease is a common movement disorder and affects many older adults. The use of deep brain stimulation has been shown to have good results in symptom reduction, but quantitative methods for the adjustment of deep brain stimulator parameters are required. In this paper, we show that features derived from wearable sensors (accelerometers) are able to characterize changes in the severity...
The objective of this pilot work is to identify characteristics and measure severity of motor fluctuations in patients with Parkinson's disease (PD) based on wearable sensor data. Improved methods of assessing longitudinal changes in PD would enable optimization of treatment and maximization of patient function. We hypothesize that motor fluctuations accompanying late-stage PD present with predictable...
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