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Gait analysis, a way of assessing the manner of walking, is considered a significant criterion in diagnosing movement disorder. Various factors contribute to the alterations in gait patterns, of which neurodegenerative related disorders play a major role. Subjects affected by Parkinson's disease (PD) suffer from numerous gait-related disturbances, eventually worsening the Quality of Life. Artificial intelligence-based tools have shown great interest in computer-assisted diagnosis with the recent advancements in technology. This review article aims at portraying a novel collective approach of accenting every facet of PD gait by emphasizing the role of quantitative gait analysis and state-of-art technologies in the betterment of clinical diagnosis. The paper includes all the relevant research works (2014-2021) regarding PD assessments categorized as 1) Classification of PD and Healthy subjects, 2) PD severity prediction, and 3) Freezing of Gait detection by only considering gait modality as the mode of assessment.

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A novel speed-invariant gait features called twofold information set (2FInS) features that capture both spatial and temporal variations in a gait cycle are proposed in this study. These features are obtained by applying first histogram of oriented gradients descriptors on the gait images followed by the representation of the underlying possibilistic uncertainty using the Hanman-Jeevan entropy function. The 2FInS features are validated on three databases: CASIA-C, OU-ISIR Treadmill-A and OU-ISIR Treadmill-D using Procrustes distance based classifier. In view of accounting both spatial and temporal information distributed throughout a gait cycle, the results obtained are superior to those of the existing methods.

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