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.
翻译:人类的动作指一种不仅代表流动性的日常运动,而且它也可以用来识别人类观察者或计算机的行走者。最近的研究表明,行走甚至传递关于行走者情感的信息。不同情绪状态的个人可能表现出不同的行走模式。各种情绪和行走模式之间的映射为自动情感识别提供了一个新的来源。与传统的情感检测生物测定方法,如面部表达、言语和生理参数相比,行走是遥测得的,更难模仿,也更不需要与主题合作。这些优势使得行走成为情感检测的一个有希望的来源。这篇文章回顾了目前关于以行走为基础的情感检测的研究,特别是行走参数如何受到不同情绪状态的影响,以及情绪状态如何通过不同的行走模式得到承认。我们侧重于在整个情感识别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,我们讨论利用智能计算和大数据方面的艺术技术,可能在未来发展高效和高效的以游戏为基础的情感识别。