A customised 1D-CNN for recognition of freezing of gait in Parkinson’s disease using multivariate decomposition techniques

Abstract The freezing of gait (FoG) presents a sudden challenge in sustaining movement which becomes a common gait issue in people with later stages of Parkinson’s disease (PD). FoG often results in falls that reduces the individual’s impact on life. A highly precise detection technique is required for accurate detection of FoG episodes automatically. This paper utilizes multivariate signal decomposition techniques, including Variational Mode Decomposition (VMD), Multivariate Variational Mode Decomposition (MVMD), and Successive Variational Mode Decomposition (SVMD). These techniques are utilized to extract time-frequency domain features from FoG signals. The Daphnet FoG dataset is used to evaluate performance in the studies. The features derived from the decomposition techniques serve as inputs to classifiers. In this study, five classifiers are employed including both machine learning and deep learning methods. The study attained the highest classification accuracy of 96.74 % with the use of a 1D CNN. The proposed approach demonstrates the potential for advancing the automated detection and facilitating early-stage diagnosis and intervention in Freezing of gait.