Application of Wavelet in Quantitative Evaluation of Gait Events of Parkinson’s Disease

Motivation. In Parkinson’s disease, disturbances in gait initiation are of particular interest as they affect postural adjustments and movement disorders which may lead to falling. This falling down may be dangerous and at times life threatening, thus becoming a major concern for the patient and the clinician. These gait abnormalities are due to dependencies of movement on the motor system. Paroxysmal dyskinesia (commonly termed as freezing of gait) is one of the extreme cases of motor blocks. Since the last two decades, automated methods for monitoring motor activities, their data analysis, and algorithm techniques have been subjects of research for Parkinson’s disease (PD). This research will be of help to clinicians in prescribing a drug regimen. Problem Statement. Development of a system based on an algorithm for automatic detection of the freezing of gait (FOG) and other postural adjustments, with the help of wearable sensor’s data and to provide a quantitative approach for assessing the intensity of PD by analyzing frequency components associated with different motor movements and gait. Methodology. This paper presents a novel wavelet energy distribution approach to distinguish between walking, standing, and FOG. Data from the acceleration sensor is taken as input. After preprocessing, discrete wavelet transform (DWT) is applied on the data which shows its entire frequency spectrum. In the next step, energy is computed for the decomposed level of interest. Results. Systems detected FOG and other gait postures and showed time-frequency range by examining differentiated decomposed signals by DWT. Energy distribution and PSD graph proved the accuracy of the system. Validation is done by the LOSO method which shows 90% accuracy for the proposed method. Conclusion. Observations of the clinical trials validate the proposed technique. In comparison to the previous techniques reported in literature, it is seen that the proposed method shows improvement in time and frequency resolution as well as processing time.