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We developed an algorithm that distinguishes between on and off states in patients with Parkinson's disease during daily life activities. Twenty-three patients were monitored continuously in a home-like situation for approximately 3 hours while they carried out normal daily-life activities. Behavior and comments of patients during the experiment were used to determine the on and off periods by a trained observer. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. Parameters related to hypokinesia (percentage movement), bradykinesia (mean velocity), and tremor (percentage peak frequencies above 4 Hz) were used to distinguish between on and off states. The on-off detection was evaluated using sensitivity and specificity. The performance for each patient was defined as the average of the sensitivity and specificity. The best performance to classify on and off states was obtained by analysis of movements in the frequency domain with a sensitivity of 0.97 and a specificity of 0.97. We conclude that our algorithm can distinguish between on and off states with a sensitivity and specificity near 0.97. This method, together with our previously published method to detect levodopa-induced dyskinesia, can automatically assess the motor state of Parkinson's disease patients and can operate successfully in unsupervised ambulatory conditions.
Keijsers et al. (2005) studied this question.