Eighteen participants (11 PD, 7 HC) completed five standardized repetitive motor tasks while wearing triaxial accelerometers attached to the extremities (wrists and ankles) to capture movement signals. Raw signals were transformed into fast Fourier (FFT) and continuous wavelet (CWT) visual outputs. Trained raters, blinded to participant diagnosis and laterality, scored the transformed images of test and retest after one week. Agreement was evaluated using average-measure intraclass correlations (ICCs) from a two-way mixed-effects model with absolute agreement. Level differences between FFT and CWT ratings were assessed using Wilcoxon signed-rank tests on patient medians.

Excellent agreement was observed between blind ratings of signals and their transforms across groups and sessions: PD Test (ICC = 0.918), HC Test (ICC = 0.840), PD Retest (ICC = 0.928), HC Retest (ICC = 0.925). Wilcoxon tests showed no significant level differences between FFT and CWT: PD Test Z = −0.211, p = .833; HC Test Z = 0.000, p = 1.000; PD Retest Z = −0.879, p = .379; HC Retest Z = −1.897, p = .058.

Blind FFT and CWT visual scoring showed strong internal agreement across raters and sessions, supporting the integration of transformed wearable data into remote, clinician-augmented PD monitoring. This approach can facilitate telehealth when clinic visits are limited by infectious diseases or conflicts and may serve as a biomarker framework for clinical trials of interventions in PD and related conditions.