During this field study, device data such as application usage, phone calls, email messages, text message, Web browsing history and location; personal data, such as contact identifiers, were anonymised. Tens of thousands of data points were gathered and categorised (for example, applications were grouped by category - entertainment, finance etc).
Coming out of the field study, a background MoodScope service was developed for both iOS and Android devices. The service consumes only 3.4 milli-Watt-hours per day, which the researchers estimated was the equivalent of losing only 20 minutes of standby time from a device's battery life. As part of the study, the researchers also developed an API that third-party applications could hook into to monitor a user's mood and react accordingly.
The paper notes the limitations of the MoodScope study's scope, such as the limited number and composition of participants in the field study. Future areas of development might reduce the frequency of mood input when training the system, reducing its intrusiveness. And as it stands, the system also has insufficient privacy protections for general release.
The researchers also noted that MoodScope can't capture every factor that affects mood: "The aim of our study is to investigate the relationship between smartphone usage patterns and user moods. We acknowledge that some external factors can go undetected with this approach. Similarly, user smartphone behaviour can change in ways that suggest dramatic shifts in mood - even when the cause is in fact unrelated to mood. Example situations include travel or critical work deadlines."
However, despite these limitations, the researchers see a promising future for MoodScope and similar systems. Detecting mood is a "vital next step for application context-awareness," they wrote.
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