Title Smartphone-Based Platform for Affect Monitoring through Flexibly Managed Experience Sampling Methods
Authors Bailon C., Damas M., Pomares H., Sanabria D., PERAKAKIS , PANTELIS, Goicoechea C., Banos O., PERAKAKIS , PANTELIS
External publication No
Means Sensors
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 2
JCR Impact 3.27500
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071175300&doi=10.3390%2fs19153430&partnerID=40&md5=3a02f64acf981e09017e82f4fcca2129
Publication date 01/01/2019
ISI 000483198900185
Scopus Id 2-s2.0-85071175300
DOI 10.3390/s19153430
Abstract 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.
Keywords affective state; context; flexible esm; flexible experience sampling; mHealth; mobile sensing; mood; smartphone
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