  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.
