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 (Kp, τ, ζnms, ωnms, 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 (ζnms) 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.
|Number of pages||6|
|Publication status||Published - 2022|
|Event||15th IFAC Symposium on Analysis, Design and Evaluation of Human Machine Systems, HMS 2022 - San Jose, United States|
Duration: 12 Sept 2022 → 15 Sept 2022
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Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)