学术文献库

Gait recognition is a term commonly referred to as an identification problem within the Computer Science field. There are a variety of methods and models capable of identifying an individual based on their pattern of ambulatory locomotion. By surveying the current literature on gait recognition, this paper seeks to identify appropriate metrics, devices, and algorithms for collecting and analyzing data regarding patterns and modes of ambulatory movement across individuals. Furthermore, this survey seeks to motivate interest in a broader scope of longitudinal analysis regarding the perturbations in gait across states (i.e. physiological, emotive, and/or cognitive states). More broadly, inferences to normal versus pathological gait patterns can be attributed, based on both longitudinal and non-longitudinal forms of classification. This may indicate promising research directions and experimental designs, such as creating algorithmic metrics for the quantification of fatigue, or models for forecasting episodic disorders. Furthermore, in conjunction with other measurements of physiological and environmental conditions, pathological gait classification might be applicable to inference for syndromic surveillance of infectious disease states or cognitive impairment.