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. 2019 Aug 5;19(15):3430.
doi: 10.3390/s19153430.

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

Carlos Bailon  1 , Miguel Damas  2 , Hector Pomares  2 , Daniel Sanabria  3 , Pandelis Perakakis  3   4 , Carmen Goicoechea  3 , Oresti Banos  5
Affiliations

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

Carlos Bailon et al. Sensors (Basel). .

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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Conflict of interest statement

The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Figures

Figure 1
Figure 1
Number of searches of “mood app” during the last 10 years. Values expressed in percentage relative to the total amount of searches on that topic. Source: Google Trends [17].
Figure 2
Figure 2
Architecture of the monitoring platform.
Figure 3
Figure 3
Russell’s circumplex model of mood [14].
Figure 4
Figure 4
Screenshots of the ESM questions for assessing valence and arousal.
Figure 5
Figure 5
ESM Management Interface.
Figure 6
Figure 6
Percentage of questionnaires answered (blue), expired (orange) and actively dismissed (grey) for each participant during the entire study.
Figure 7
Figure 7
Overall percentage of questionnaires answered (blue), expired (orange) and actively dismissed (grey) per interval of daily hours.
Figure 8
Figure 8
Overall response rate registered per day of study. The red dashed vertical line splits the graphic in the two weeks of the study.
Figure 9
Figure 9
Completion times of the questionnaires per day of study for the valence question. Times over 300 s have not been considered.
Figure 10
Figure 10
Completion times of the questionnaires per day of study for the arousal question. Times over 300 s have not been considered.
Figure 11
Figure 11
Time elapsed from the reception of the notification to the participant’s response per interval of daily hours.
Figure 12
Figure 12
System Usability Scale (SUS) score obtained from each participant. The horizontal lines represent the mean SUS score of the system and the threshold value that indicates a good usability.
See this image and copyright information in PMC

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