A portable system for quantitative assessment of parkinsonian rigidity

Abstract

Rigidity is one of the primary symptoms of Parkinson's disease. Passive flexion and extension of the elbow is used to assess rigidity in this study. An examiner flexes and extends the subject's elbow joint through a rigidity assessment cuff attached around the wrist. Each assessment lasts for 10 seconds. Two force sensor boxes and an inertial measurement unit are used to measure the applied force and the state of the elbow movement. Elastic and viscous values will be obtained through a least squares estimation with all the data. 9 healthy subjects were tested with this system in two experimental conditions: 1) normal state (relaxed); 2) imitated rigidity state. Also the subjects were performed the assessment task with different frequencies and elbow movement ranges. The imitated rigidity action increases viscosity and elasticity. The effect sizes (Cohen's d) of the viscosity and elasticity between normal state and imitated state are 1.61 and 1.36 respectively, which means the difference is significant. Thus, this system can detect the on-off fluctuations of parkinsonian rigidity. Both wrist movement angle and frequency have small effect on the viscosity, but have elevated effect on the elasticity.

Automatic psychomotor function quantification in Parkinson’s disease via natural interaction with digital devices

“…The quantitative metrics derived from the statistical analysis of the sensor signals showed better correlation with neurologists' evaluations than other parkinsonian hand performance assessment systems (Dai, Lin, and Lueth, 2015a). In 2013, they presented a more sophisticated system using two force sensor boxes combined with an IMU to evaluate upper limb rigidity (Houde Dai et al, 2013). In the remote assessment end, Roy et al presented a hybrid system that combined electromyographic and accelerometer data analyzed with neural network algorithms to generate an evolving longitudinal evaluation of motor performance that was able to discriminate normal movement from PD-impaired movement (Roy et al, 2013).…”