  Human gait refers to a daily motion that represents not only mobility, but it
can also be used to identify the walker by either human observers or computers.
Recent studies reveal that gait even conveys information about the walker's
emotion. Individuals in different emotion states may show different gait
patterns. The mapping between various emotions and gait patterns provides a new
source for automated emotion recognition. Compared to traditional emotion
detection biometrics, such as facial expression, speech and physiological
parameters, gait is remotely observable, more difficult to imitate, and
requires less cooperation from the subject. These advantages make gait a
promising source for emotion detection. This article reviews current research
on gait-based emotion detection, particularly on how gait parameters can be
affected by different emotion states and how the emotion states can be
recognized through distinct gait patterns. We focus on the detailed methods and
techniques applied in the whole process of emotion recognition: data
collection, preprocessing, and classification. At last, we discuss possible
future developments of efficient and effective gait-based emotion recognition
using the state of the art techniques on intelligent computation and big data.
