Designing for Emotion

What We Need & Who We Should Consider

The three intended audiences addressed in this research have the following needs for emotion recording tools:

Emotion Research

The inevitable challenges for the emotion research and social science community are the lack of consensus on definition and means of measurement of emotion and the reliance on layperson and self-reporting for empirical studies. Scherer, who I discussed earlier and often for his work on the Geneva Emotion Wheel, wrote about characteristics for an instrument that, in spite of these challenges, would assist in the empirical study of emotion. He listed focusing on felt qualities of an emotion, using more than two dimensions and relying on natural language as all essential for further developments to made. His final characteristic emphasizes that the tool function in a user-friendly, graphical form. Users should be able to rapidly understand how to use it, therefore increasing its reliability as a form of emotion recording. To extend the tool’s reliability, each emotion recording should be framed with the same parameters and constraints, optimizing entries for memorization and comparison. The more emotion data available using the same system will help identify more areas for research within the subject field.

Social Sciences: Psychology & Psychiatry

In my interviews with professionals and patients, I found that there were many unmet and unrealistic expectations on both sides about therapy and its potential outcomes. In order for an emotion recording tool to be a successful addition to the therapy space, it needs to put emphasis on the whole process of emotion, including the stimulus and the label. This helps patients to differentiate between the stages of an emotion experience and express them as such. A typical part of therapy is revisiting one’s past. Therapists felt that a tool would need to place value on creating an “emotion history,” demonstrating to patients that context is key to overt behaviors. This inevitably requires the tool to function as a repetitive process, reinforcing the need of user-friendliness and a system with the same parameters for each recording. An addition to Scherer’s suggestion of natural language would be the use of metaphor to help users recognize an emotion. Metaphor would serve as a stepping stone to labeling the emotion when user’s vocabularies are not extensive. If this is the case, a scale for intensity would be most helpful when users are identifying more nuanced emotions.

Patients & Users

Patient and user needs are based on both interviews throughout my research and general principles for usability. If an instrument used frequently is not self-explanatory, it should become rapidly easier to use after on-boarding. In order for consistent and continual use to be encouraged, the tool should demonstrate its value in its first use and in its richness overtime. When this habit is established, a user of an emotion tracking tool is able to seamlessly create an emotion history. Patients in therapy, like users of quantified-self trackers, need the element of self-discovery. A emotion recording device should contain opportunities for recognition and self-realization. Like any language learning, a system employed for tracking emotion should regulate a vocabulary and encourage users to maintain it. Often therapy itself is used as a tool to get through challenging situations. Any additional device used should be reinforcing what is learned within a therapeutic session and strengthen good behavioral patterns and coping mechanisms.

The Golden Rules

Fine. is a digital application, and therefore a generator for emotion data. As an addition to the above needs, I developed four “golden rules” for tools tracking and keeping records of this type of personal data. These rules were formulated from exchanges throughout the research process when considering what possibilities, good and bad, an emotion recording tool creates.

Emotion data is not for sale.

For many people, making their moods, feelings and emotion experiences “computer-readable” is new. Digital tools should first and foremost respect the privacy of a user as if they were using an analog medium for recording. Share-ability is not a focal point of Fine., but users’ desire for it could be explored in the future.

Emotion awareness should be encouraged.

As we discussed in the chapter on emotion, our brains are wired to avoid uncomfortable emotion experiences. Emotion awareness leads to self-discovery and a greater self-knowledge. Digital applications should not identify further opportunities to negate it.

Promote engagement and discovery.

Similar to the encouragement of awareness, users of an emotion tracking tool should not be provided with suggestions for to how to change aspects of their life. The search of self-identity requires that users engage with their emotions, and in this case, their emotion data. Technology can assist us in pattern finding, but we should not build algorithms to shortcut reflection upon them.

The user retains the right to choose.

Perhaps it is a dystopian line of thinking, however, in the world of the Internet of Things, it is not too far fetched. Acting upon one’s own discretion is fundamental to a human’s existence. We don’t need to further our dependency on technology to do it for us, especially concerning a sensation unique to living organisms.

When building any user-centered tool, designers should consider the ethical consequences of their products. That being said, it is impossible to test for every use case and the odds are that our tools will be misused. The book accompanying this thesis, Could Be Worse, fictitiously plays out what could happen if these rules are ignored.



An overview of this project and a link to the log book of my process can be found online here.

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