One of the distinguishing features of wearable computers, as opposed to merely portable computers, is that they can be in physical contact with you in a long-term intimate way. A wearable may not just hang on your belt, but it may also reside in your shoes, hat, gloves, jewelry, or other clothing, providing a variety of kinds of physical contact beyond the traditional paradigm of fingertips touching only a keyboard and a mouse. In particular, when equipped with special sensors and tools from signal processing and pattern recognition, a wearable computer can potentially learn to recognize physical and physiological patterns--especially those which correspond to affective states--such as when you are fearful, stressed, relaxed, or happily engaged in a task.
Sensing physiological patterns is not a new thing; ambulatory medical devices have been under development for years, helping people with various medical complications to monitor heart rate, blood pressure, and more. Affective wearables overlap with medical wearables in that both may sense physiological signals. In particular, both may be concerned with sensing signals that indicate stress or anxiety [HB96], an application of interest not just for people suffering from anxiety attacks or other medical conditions, but also for healthy people who are interested in staying healthy. Affective states of depression, anxiety, and chronic anger have been shown to impede the work of the immune system, slowing down healing and making people more vulnerable to viral infections (See Chap 11 of [Gol95].) Wearables provide a means of monitoring stress and other conditions outside the confines of a medical facility, gathering data as the wearer goes about his or her daily activities. Of course, none of the data collection or analysis implies that the user will choose to change their behavior or lifestyle, but it can help a wearer make informed decisions, and can be shared with one's physician, if the wearer desires, for help in treating chronic problems like back pain and migraine headaches which can be stress related.
Sensing affective patterns, such as stress, also has important implications for developing intelligent and effective human-computer interfaces, as will be described in applications, below. There is a movement in computer science toward developing systems that learn what their users want, and try to model their user's interests. However, a natural way that people express want they want, and whether they like or dislike things, is through affective expression. They may speak with a pleased or distressed voice. They may smile or frown. They may gesture, nod, slump, or otherwise indicate that they feel good or bad. A big problem is that current computers are oblivious to most of these affective cues. They ask us to click on menus to indicate whether we like something or not, when instead they could have sensed our response from its most natural form of expression. A wearable has an unprecedented opportunity to ``get to know'' its wearer.
One of the problems in giving a computer the ability to recognize affective patterns is that, despite decades of research, emotion theorists still do not understand what emotions are and how they are communicated. One of the big problems in emotion theory is determining what physiological patterns accompany each emotion (See, for example, [CT90].) In some individuals, an increase in temperature and blood pressure might co-occur with anger. An acceleration in heart rate and pupillary dilation might indicate the person likes what he is looking at. However, almost all of the studies trying to determine which responses occur with which emotions have been done on artificially elicited emotions in a lab setting, where there is good reason to believe that people might not feel the emotions in the same way as when they are more naturally elicited. This problem has held back progress in emotion understanding. A wearable allows a tremendous opportunity to learn about affective patterns in natural situations. Affective wearables provide a perfect opportunity to bring powerful computational methods to bear on testing emotion theories.