Parkinson's Disease (PD) patients often need Deep Brain Stimulation (DBS) surgery when they become intolerant to drugs or these lose efficiency. A stimulation electrode is implanted in the basal ganglia to promote the functional control of the deregulated dopaminergic motor pathways. The stimulation target is defined by medical imaging, followed by electrophysiological inspection for fine electrode position trimming and electrical stimulation tuning. Intra-operative stimulation of the target and the evaluation of wrist rigidity allows to choose the stimulation parameters which best alleviate PD symptoms without side effects. Neurologists impose a passive wrist flexion movement and qualitatively describe the perceived decrease in rigidity under different voltages, based on its experience and with subjectivity. We designed a novel, comfortable and wireless wearable motion sensor to classify the wrist rigidity by deriving a robust signal descriptor from angular speed values and a polynomial mathematical model to classify signals using a quantitative continuous scale. The descriptor significantly (p<;0.05) distinguished between non-rigid and rigid states, and the classification model labelled correctly 83.9% of the evaluated signals against the blind-agreement of two specialists. Additionally, we developed a methodology to detect cogwheel rigidity from the angular speed signal with high sensitivity (0.93). Our system provides a reliable evaluation of wrist rigidity, improving upon the inherent subjective clinical evaluation while using small, simple and easy to use motion sensor.