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Smartphone-Based Platform for Affect Monitoring through Flexibly Managed Experience Sampling Methods

机译:基于智能手机的平台可通过灵活管理的体验采样方法进行效果监控

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摘要

The identification of daily life events that trigger significant changes on our affective state has become a fundamental task in emotional research. To achieve it, the affective states must be assessed in real-time, along with situational information that could contextualize the affective data acquired. However, the objective monitoring of the affective states and the context is still in an early stage. Mobile technologies can help to achieve this task providing immediate and objective data of the users’ context and facilitating the assessment of their affective states. Previous works have developed mobile apps for monitoring affective states and context, but they use a fixed methodology which does not allow for making changes based on the progress of the study. This work presents a multimodal platform which leverages the potential of the smartphone sensors and the Experience Sampling Methods (ESM) to provide a continuous monitoring of the affective states and the context in an ubiquitous way. The platform integrates several elements aimed to expedite the real-time management of the ESM questionnaires. In order to show the potential of the platform, and evaluate its usability and its suitability for real-time assessment of affective states, a pilot study has been conducted. The results demonstrate an excellent usability level and a good acceptance from the users and the specialists that conducted the study, and lead to some suggestions for improving the data quality of mobile context-aware ESM-based systems.
机译:识别触发情感状态发生重大变化的日常生活事件已成为情感研究的一项基本任务。要实现此目标,必须实时评估情感状态以及可能情境化获取的情感数据的情境信息。但是,对情感状态和情境的客观监控仍处于早期阶段。移动技术可以帮助完成这项任务,提供用户上下文的即时和客观数据,并有助于评估他们的情感状态。先前的工作已经开发了用于监控情感状态和情境的移动应用程序,但是它们使用固定的方法,该方法不允许根据研究的进展进行更改。这项工作提出了一个多模式平台,该平台利用了智能手机传感器和体验采样方法(ESM)的潜力,以无处不在的方式提供对情感状态和上下文的连续监视。该平台集成了多个元素,旨在加快ESM问卷的实时管理。为了展示该平台的潜力,并评估其可用性和对情感状态实时评估的适用性,我们进行了一项试点研究。结果表明,该产品具有出色的可用性,并得到了进行这项研究的用户和专家的良好接受,并为提高基于移动上下文感知ESM的系统的数据质量提供了一些建议。

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