A bra that can prevent overeating? Don’t laugh, there are people working on it. True story. A group at the University of Rochester Department of Computer Science are researching a “just-in-time support system for emotional eating”. The researchers conducted user studies and built a prototype with the wearable technology.
The following information is from the report.
RESEARCH APPROACH
Designing a system to provide just-in-time interventions for emotional eating is an ambitious endeavor. Consider the following hypothetical scenario:
Sally has been home from work for a few hours, and she finds herself rather bored. An application on Sally’s mobile phone has also detected that she is bored by reading her physiological state through wearable sensors. Since this mobile application has previously learned that Sally is most susceptible to emotional eating when she is bored, the application provides an intervention to distract Sally and hopefully prevent her from eating at that moment.
From this scenario, we see three key requirements for a just-in-time support system for emotional eating. First, the application has to be aware of the user’s emotional eating
patterns. Does Sally emotionally eat only when she is bored? Second, the system needs to be able to implicitly detect emotions. This involves wearable physiological sensors that are connected to the mobile phone. Implicit detection of emotions would then be possible through machine learning classification, which requires training on large amounts of users’ data.
Finally, it is critical (and perhaps the most challenging) to determine how to intervene. What type of intervention do we design? How often do we intervene? How do we prevent it from becoming an annoyance to the user? Our approach to researching a just-in-time support system for emotional eating was to make strides towards addressing these three requirements. We studied these requirements across three user studies, which have been summarized below.
• First, the application has to be aware of the user’s emotional eating patterns. Does Sally emotionally eat only when she is bored?
• Second, the system needs to be able to implicitly detect emotions. This involves wearable physiological sensors that are connected to the mobile phone. Implicit detection of emotions would then be possible through machine learning classification, which requires training on large amounts of users’ data.
• Finally, it is critical (and perhaps the most challenging) to determine how to intervene. What type of intervention do we design? How often do we intervene? How
Study 1: Gather Emotional Eating Patterns.
We investigated eating behaviors and corresponding emotions of participants by having them self-report their emotions and log their eating patterns using a custom built application called EmoTree. The goal was to understand their emotions associated with eating.
Study 2: Investigate An Intervention Technique.
The purpose of Study 2 was to learn about a particular intervention technique for emotional eating. We prototyped implicit intervention by triggering an intervention based on self reported ratings of emotions. This allowed us to gather early feedback about interventions before implementing an automatic system. Are users aided by the intervention? Was the intervention sent at the appropriate time? What other types of interventions would interest users?
Study 3: Emotion Detection with Wearables.
This work was a first step in building an automatic system. We investigated the feasibility of using physiological sensor data, combined with machine learning, to automatically detect emotions in a mobile system. We also present the design of our wearable system.
The hypothetical feedback, whether from a social network, a close friend, or pre-recorded messages, served as a health intervention to encourage the person to be more active or consume less food.
Behavior modification in health is difficult, as habitual behaviors are extremely well-learned, by definition. This research is focused on building a persuasive system for behavior modification around emotional eating. In this paper, we make strides towards building a just-in-time support system for emotional eating in three user studies. The first two studies involved participants using a custom mobile phone application for tracking emotions, food, and receiving interventions. We found lots of individual differences in emotional eating behaviors and that most participants wanted personalized interventions, rather than a pre-determined intervention. Finally, we also designed a novel, wearable sensor system for detecting emotions using a machine learning approach. This system consisted of physiological sensors which were placed into women’s brassieres. We tested the sensing system and found positive results for emotion detection in this mobile, wearable system.
Wearable technology is the wave of the future but is this taking it too far? Let us know your thoughts on twitter @hardbodynews or in the comments below.
Lead image via www.cs.rochester.edu
