For Parkinson's patients to function at their best, their medications need to be optimally adjusted to the diurnal variation of symptoms. For this to occur, it is important for the managing clinician to have an accurate picture of how the patient's bradykinesia/hypokinesia and dyskinesia and the patient's perception of movement state fluctuate throughout the normal daily activities. The present invention uses wearable accelerometers coupled with computer implemented learning and statistical analysis techniques in order to classify the movement states of Parkinson's patients and to provide a timeline of how the patients fluctuate throughout the day.
Method and apparatus for classification of movement states in Parkinson's disease