There are dozens of applications of affective computing in addition to the medical and health applications mentioned above[Pic97].). For example, emotions are known to provide a keen index into human memory; therefore, a computer that pays attention to your affective state will be better at understanding what you are likely to recall on your own. For example, instead of recording everything you hear, it might learn to record (or play back) just those places where your mind wandered. Augmenting a system like Steve Mann's WearCam [Man97] with affective sensing and pattern recognition could help it learn when to ``remember'' the video it collects, as opposed to always relying on the user to tell it what to remember or forget. Suppose for example that you let the camera roll while playing with a cute little baby. It might notice that you always save the shots when the baby makes you laugh, or smile. By detecting these events, it could become smarter about automatically saving these photos. Moreover, by labeling the photos with these affective events, you can later ask for ones by their affective qualities, ``Computer, please show us the funny images.'' Of course the wearer should be free to override these ``smart'' settings, but if they are learned continuously, by watching what the wearer chooses, they will help reduce some of the users workload and enable the wearer to offload repetitive tasks.
Analysis of a wearer's affective patterns could also trigger actions in real time. For example, a ``fear detector'' might trigger the WearCam to save a wide-angle view of the environment, and to transmit the wearer's position, viewpoint, and fear state to a personal ``safety net,'' a community of friends or family with whom you felt secure. To a wearer in a role-playing game, a fear detector might change his appearance to other players, or might reward him for overcoming his fear, with bonus points for courage.
An intelligent web browser responding to the wearer's degree of interest could elaborate on objects or topics that the wearer found interesting, until it detected the interest fading. An affective assistant agent could intelligently filter your e-mail or schedule, taking into account your emotional state or degree of activity.
Another application we are exploring in our research is the relationship between long-term affective state or ``mood'' and musical preferences. Music is perhaps the most popular socially-accepted form of mood manipulation. Although it is usually impossible to predict exactly which piece of music somebody would most like to hear, it is often not hard to pick what type of music they would prefer--a light piano sonata, an upbeat jazz improvisation, a soothing ballad--depending on what mood they are in. As wearable computers gain in their capacity to store and play music, to sense the wearer's mood, and to analyze feedback from the listener, they have the opportunity to learn patterns between the wearer's mood, environment, and musical preferences. The ultimate in a musical suggestion system, or ``affective CD player'' would be if it not only took into account your musical tastes, but also your present conditions - environmental and mood-related.
The possibilities are diverse - a wearer who jogs with her wearable computer might like it to surprise her sometimes with uplifting music when her wearable detects muscle fatigue and she starts to slow down. Another wearer might want the system to choose to play soft relaxing music whenever his stress indicators hit their highest levels. He might also want the computer to evaluate its own success in helping him relax, by verifying that, after some time, he did achieve a lower stress level. If the wearer's stress level increased with the music, or with a suggestion of music, then the computer might politely try another option later.
The whole problem of building systems which adapt to you is an important domain for affective wearables. Many times technology only increases stress, making users feel stupid when they do not know how to operate the technology, or making them actually become stupid when they rely on it in a way that causes their own abilities to atrophy. Our goal is to give computers the ability to pay attention to how the wearer feels, and to use this information to better adapt to what its wearer wants. Along the way, however, we need to be careful in considering the role of wearables in augmenting vs. replacing our abilities. For example, we know when a human is highly aroused (as in a very shocking or surprising situation) that she is more likely to remember what is happening-the so-called ``flash-bulb memory[BK77].'' If the human brain is recording with full resolution at these times, then the wearable imaging system may not need to record more than a snapshot. In contrast, when the human is snoozing during a lecture, the wearable might want to kick in and record the parts the wearer is missing.
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