Finger Tapping feature extraction in Parkinson's disease using low-cost accelerometers

The clinical hallmarks of Parkinson's disease (PD) are movement poverty and slowness (i.e. bradykinesia), muscle rigidity and limb tremor. The physicians usually quantify these motor disturbances by assigning a severity score according to validated but time-consuming clinical scales such as the Unified Parkinson's Disease Rating Scale (UPDRS) - part III. These clinical ratings are however prone to subjectivity and inter-rater variability. The PD medical community is therefore looking for a faster and more objective rating method. As a first step towards this goal, a tri-axial accelerometer-based system is proposed as patients are engaged in a repetitive finger tapping task, which is classically used to assess bradykinesia in the UPDRS-III. After developing the hardware, an algorithm has been developed, that automatically epoched the signal on a trial-by-trial basis and quantified, among others, movement speed, amplitude, hesitations or halts as validated by visual inspection of video recordings during the task. The results obtained in a PD patient and an healthy volunteer are presented. Preliminary results show that PD patients and healthy volunteers have different features profiles, so that a classifier could be set up to predict objective UPDRS-III scores.