A PATIENT-INVARIANT MODEL FOR FREEZING OF GAIT DETECTION AIDED BY WAVELET DECOMPOSITION

Freezing of Gait (FoG) is a paroxysmal and devitalizing symptom associated with Parkinson's disease (PD). Episodes of FoG impedes gait and augments fall propensity, often leading to serious fall-injury. In this paper, we present a method for online detection of FoG using a wearable motion sensor. The novelty lies in utilizing the Empirical Wavelet Transform for signal denoising and incorporating the two new features to ameliorate the accuracy of the algorithm. Fundamentally, we have focused on a patient-independent model and leveraged a single ankle sensor which makes it a more feasible approach in terms of usability. Our model is evaluated on Daphnet dataset and achieved the average Sensitivity of .95 and Specificity of .70 with only a single sensor, demonstrating its immense potential.