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We propose a novel system that analyzes the components of rigidity in Parkinson's disease (PD) usually perceived by physicians, in a very simple and systematic way for routine clinical practice. Our system is composed of two compact force sensors, a gyroscope, and EMG surface electrodes. Muscle tone was assessed in 24 healthy elderly subjects and 27 PD patients by passive extension and flexion of the elbow joint with ramp-and-hold trajectory. Torque and angle data in the dynamic phase were used to calculate "elastic coefficients" in extension and flexion, and the mean value of torque in each dynamic phase at each joint angle (defined as "Bias") was also calculated. The muscle activity index in the static phase (EMG Index) was obtained for both biceps brachii (BB) and triceps brachii (TB) muscles. "Elastic coefficients," sum of the "difference of Bias" and "EMG Index" for BB all correlated well with UPDRS score. Based on our results, Parkinsonian rigidity consists of both an "elastic" component and a "difference of Bias" component, and neurologists may assess greater rigidity when either one or both components are high in value. The EMG index was useful for differentiating PD patients with slight rigidity from healthy elderly adults.

The purpose of this study was to develop a finger taps acceleration measurement system for the quantitative diagnosis of Parkinson's disease. The system was composed of two 3-axis piezoelectric element accelerometers, a pair of touch sensors made of thin stainless steel sheets, an analog-digital(AD) converter and a personal computer (PC). Fingerstalls,with these sensors, were attached to subject's index finger and thumb. The acceleration and output of the touch sensors were recorded using the PC during the finger taps movements. Intervals between the single finger taps movements were calculated from the measured output of the touch sensors. Velocities during the single finger taps movements were calculated by integrating the measured acceleration. The amplitudes were calculated by integrating the velocities. The standard deviation of the single finger taps intervals, average of maximum single finger taps velocities and average of maximum single finger taps amplitudes were calculated from them. They were used as features for the quantitative diagnosis of Parkinson's disease. The developed system was used to conduct finger taps tests employing 27 normal subjects and 16 Parkinson's diseases subjects. The subjects were asked to execute continuous finger taps movement for 60 s. It was shown that the acceleration and output of the touch sensors could be measured and the features could be extracted.

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