Survey for rating emotions

This survey will be used as preliminary data for determining how people view their emotions on an X/Y scale, based on arousal (whether the emotion is in a neutral zone or an invested zone) and energy (if the energy of the emotion is high or low).

The categories are:
A1 = Neutral, Low Energy
B1 = Neutral, High Energy
A2 = Invested, Low Energy
B2 = Invested, High Energy

Once the data is gathered, it will influence how words will be selected for users when accessing the mood app as related to their arousal and energy. The graph, however, will be scaled up to account for 25 data points per positive and negative emotion, rather than 4.

https://www.surveymonkey.com/s/2FLQXM3

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Mood Measurement

Instead of creating a stress reducing application, which would require an obscenely long timetable and resources that I, as a fresh-faced data and health developer, do not have, there needs to be a gear shift.

Research done has shown that two of my primary data sources for what was going to be a stress reduction app, ambient noise and heart rate, are not very solid indicators of stress by themselves. There is too much individuality in stress that can only be measured either by oneself or by brain instruments. Therefore, we take a step back, and we look instead explicitly at mood.

Now, looking at applications on smartphones and tablets today, while useful, they are slightly cumbersome to use. You do not innately want to measure your mood on the device. Why would you? People have enough issue with wanting to open their apps to view their Fitbit stats or log their food, let alone log their mood, which is as fleeting and varying as an ocean.

This is where smartwatches can come into play. Rather than requiring a user to go pull their device out of their pocket and find the app, instead their wrist can let them know to log that data. In tracking the trends of a user based on similar data, such as ambient noise and movement, a device can prompt the user in an easy to use, quick interface that doesn’t take more than a few seconds out of their day.

Thinking about the data that wearable devices gather in the background every day, they can quickly learn the trends of a user, and rather than begin prompting them for what their mood is, the application will instead prompt the user with “Is this your mood?” As the device is reaching that level of intelligence, it can also begin providing users with tips on how to improve their way of living, and in that way, we come back to the original application idea: de-stress.