感知集群

Aftab Khan, Alexandros Zenonos, G. Kalogridis, Yaowei Wang, Stefanos Vatsikas, M. Sooriyabandara
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引用次数: 0

摘要

近年来,人们对自动情绪识别进行了研究,特别是对压力的研究。其他情感状态也非常重要,因为研究它们可以帮助更详细地理解人类行为。大多数为实现能够识别人类情绪的自动化系统而进行的研究都表明,情绪是个人的——也就是说,情绪感知在个体之间有所不同。先前基于机器学习的框架证实了这一假设,个性化模型几乎总是优于通用方法。在这篇文章中,我们提出了一种新的系统,根据个体的生理信号将其分组为我们所称的“感知簇”。我们在工作环境中对九名用户进行了试验,记录了至少10天的生理和活动数据,以评估感知集群。我们的结果表明,与个性化方法相比,性能没有显著差异,而且我们的方法与传统的通用方法相比表现同样更好。这种方法显著降低了个性化方法所需的计算需求,而个性化方法需要为每个用户单独开发单独的模型。此外,感知集群表明了向半监督情感建模的方向,在半监督情感模型中,个体感知是从数据中推断出来的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perception Clusters
Automated mood recognition has been studied in recent times with great emphasis on stress in particular. Other affective states are also of great importance, as studying them can help in understanding human behaviours in more detail. Most of the studies conducted in the realisation of an automated system that is capable of recognising human moods have established that mood is personal—that is, mood perception differs amongst individuals. Previous machine learning--based frameworks confirm this hypothesis, with personalised models almost always outperforming the generalised methods. In this article, we propose a novel system for grouping individuals in what we refer to as “perception clusters” based on their physiological signals. We evaluate perception clusters with a trial of nine users in a work environment, recording physiological and activity data for at least 10 days. Our results reveal no significant difference in performance with respect to a personalised approach and that our method performs equally better against traditional generalised methods. Such an approach significantly reduces computational requirements that are otherwise necessary for personalised approaches requiring individual models developed separately for each user. Further, perception clusters manifest a direction towards semi-supervised affective modelling in which individual perceptions are inferred from the data.
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CiteScore
10.30
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