Quantifying Motor Skills in Early-Stage Parkinson's Disease Using Human Controller Modeling

Daan M. Pool*, Rick J. de Vries, Johan J.M. Pel

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

1 Citation (Scopus)
16 Downloads (Pure)


This paper investigates the potential of using a manual pursuit tracking task for quantifying loss of motor skills due to Parkinson's disease (PD), by applying human controller (HC) modeling techniques. With this approach, it is possible to obtain detailed quantitative data on motor performance in terms of control gain, response delay, stiffness and damping. Pursuit tracking data was collected from seven early-stage PD patients and a matched control group at the Erasmus MC in Rotterdam. Tracking performance was significantly worse in the PD group compared to the controls. Furthermore, the PD patients showed significantly lower control gains and degraded neuromuscular damping and bandwidth, which indicates that early-stage PD is associated with loss of quick and fast arm movements. While the PD patients showed less consistent and linear control behavior in the task, their data could still be modelled at high accuracy. Using HC models to quantify PD patients' fine motor skill abilities may contribute to improved (early) detection of motor skill loss in PD, as well as detailed monitoring of symptom development and intervention effectiveness.

Original languageEnglish
Pages (from-to)96-101
Number of pages6
Issue number29
Publication statusPublished - 2022
Event15th IFAC Symposium on Analysis, Design and Evaluation of Human Machine Systems, HMS 2022 - San Jose, United States
Duration: 12 Sep 202215 Sep 2022


  • cybernetics
  • human controller modeling
  • medicine
  • motor skills
  • Parkinson's disease


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