Parkinson's disease (PD) is a progressive nervous system disorder that affects movement. PD has a severely negative impact on the quality of life of patients and their caregivers. The timing of treatment depends, amongst others, on the quantification of patients' motor performance. To date, the resolution used in scaling motor performance is too low to detect subtle behavioral changes over time. This paper investigates if 'longitudinal' data-sets of motor performance data obtained from tracking tasks can detect behavioural changes in motor performance data representative for PD symptoms. Such longitudinal data were approximated using a combined data-set based on 50 trials of collected experiment data from 25 healthy participants (age range 55-75 years), augmented with 25 bootstrapped samples scaled to represent 'Mild' or 'Severe' motor performance degradation. An approach based on general linear regression models was tested for its capacity to detect the adverse trends in typical tracking task metrics (K_p, τ, ζ_nms, ω_nms, RMSe, and RMSu). Overall, it was found that with this approach in at least 50% of all participants, a simulated change in motor behaviour was successfully detected, a number that may increase to 97% for the most sensitive metric (ζ_nms) and consistent participant data. This indicates that the developed approach is promising towards the development of more objective and detailed monitoring of disease progression and treatments in PD patients.

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.

In the analysis of human motor skills, tracking tasks with multisine target signals are often performed as they allow for quantitative measurement, identification, and modeling of human control dynamics. In this paper, the same "cybernetic" approach is taken to analyze eye movement dynamics in gaze tracking tasks, where participants had to track a moving target marker across the screen) with their eyes (i.e., a eye-only task) and 2) with their dominant hand (i.e., a eye-hand task). A human-in-the-loop experiment with 10 participants was performed to measure the eye movement dynamics. These two different conditions were performed with four different bandwidths of the multisine target signal driving the movement of the visual stimulus. The results show that the measured eye movement dynamics can be identified from the data of all experiment conditions and can be accurately modeled as an underdamped mass-spring-damper system with a time delay. Furthermore, with increased target signal bandwidth the bandwidth of participants' eye movements also increases. Future development of gaze tracking tasks into a new tool for assessment of altered gaze behavior due to neurological diseases should take the balance between saccadic and smooth pursuit eye movements into account and avoid very high (i.e., too difficult) bandwidths to warrant accurate modelling of gaze dynamics.