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.

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步态分析的情绪识别：当前研究和未来方向

人类步态是指日常运动，它不仅代表移动性，而且还可以被人类观察者或计算机用来识别步行者。最近的研究表明，步态甚至可以传达有关步行者情绪的信息。处于不同情绪状态的个体可能表现出不同的步态模式。各种情绪和步态模式之间的映射为自动情绪识别提供了新的来源。与传统的情绪检测生物识别技术（如面部表情、语音和生理参数）相比，步态可远程观察，更难以模仿，并且需要受试者的合作较少。这些优势使步态成为情绪检测的有希望的来源。本文回顾了当前基于步态的情绪检测的研究，特别是关于步态参数如何受到不同情绪状态的影响，以及如何通过不同的步态模式识别情绪状态。我们专注于情感识别整个过程中应用的详细方法和技术：数据收集、预处理和分类。最后，我们使用最先进的智能计算和大数据技术讨论了高效和有效的基于步态的情绪识别的未来发展。