Many interactions with mobile devices could be more like checking the time on a wristwatch - short and almost without conscious thought. I term activities like these microinteractions. For my dissertation work, I propose to investigate and design interfaces to facilitate microinteractions. I will do this through developing interfaces for small, on-body devices. Many interactions that might otherwise be micro- are stymied by too-long “setup” times - the amount of time it takes to retrieve the device and navigate to the appropriate application - that must take place before the desired interaction can be accomplished. My proposed work will shorten or remove altogether the setup time.
The lack of support for microinteractions in current technology leads to a failure to use the provided functionality, to the detriment of the user. To better enable microinteractions, I propose to develop two new techniques for wrist-based interactions. I choose the wrist because it is a socially acceptable platform for wearable devices, and because it enables very fast access. The two interactions are touchscreen-based and gesture-based.
One issue with mobile interactions is how to avoid false activations, when the device is unintentionally activated by some natural action of the user. Many current devices attempt to solve the problem by having “push-to-activate” mechanisms; however, this practice results in added complexity and, in some cases, the loss of single-handed functionality. The methods that I explore in this proposal remove the need for push-to-activate functionality, allowing the user to initiate microinteractions with no prelude.
For the touchscreen-based interactions, I propose to use a round-faced touchscreen watch. Historically, most clocks and watches have been circular, while all commercially available touchscreen watches thus far have been rectangular. I present results of a study on the best method for placing buttons around the rim of a round touchscreen watch, and I propose two related interaction techniques that simultaneously encourage users to utilize the most efficient method of interaction and prevent undesired interaction due to accidental touches of the screen.
Mobile gesture-based systems in particular suffer from the issue of unintentional activation: the normal movements of the user can be confused for intentional gestures. This problem is most often solved with push-to-activate; however, in the case of wrist-based gesture systems, pushing requires the use of the non-wristwatch hand, meaning that the gesture functionality could easily be replaced by buttons. I propose a solution to the unintentional activation problem that, rather than involving pushing-to-activate, uses an “Everyday Gesture Library” to help interaction designers choose gestures that are unlikely to be confused with normal gestures made in the course of daily life.

The wristwatch is a device that is quick to access, but is currently under-utilized as a platform for interaction. We investigate interaction on a circular touchscreen wristwatch, empirically determining the error rate for variously-sized buttons placed around the rim. We consider three types of inter-target movements, and derive a mathematical model for error rate given a movement type and angular and radial button widths.

We investigate the effect of placement and user mobility on the time required to access an on-body interface. In our study, a wrist-mounted system was significantly faster to access than a device stored in the pocket or mounted on the hip. In the latter two conditions, 78% of the time it took to access the device was spent retrieving the device from its holder. As mobile devices are beginning to include peripherals (for example, Bluetooth headsets and watches connected to a mobile phone stored in the pocket), these results may help guide interface designers with respect to distributing functions across the body between peripherals.

Anytime, anywhere experience capture is becoming the norm, especially capture involving multimedia. What's the future of mobile experience-capture technology? For example, if a company were to make a small, white, beautifully designed capture device with an “i” in front of its name, what would it be? Let's explore this question by examining three technologies: one that's currently available, one that could be here tomorrow, and one that could be a product in the future.

Wearable computers have the potential to act as intelligent agents in everyday life and assist the user in a variety of tasks, using context to determine how to act. Location is the most common form of context used by these agents to determine the user's task. However, another potential use of location context is the creation of a predictive model of the user's future movements. We present a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales. These locations are then incorporated into a Markov model that can be consulted for use with a variety of applications in both single{user and collaborative scenarios.

The wristwatch is a device that is quick to access, but is currently under-utilized as a platform for interaction. We investigate interaction on a circular touchscreen wristwatch, empirically determining the error rate for variously-sized buttons placed around the rim. We consider three types of inter-target movements, and derive a mathematical model for error rate given a movement type and angular and radial button widths.

We investigate the effect of placement and user mobility on the time required to access an on-body interface. In our study, a wrist-mounted system was significantly faster to access than a device stored in the pocket or mounted on the hip. In the latter two conditions, 78% of the time it took to access the device was spent retrieving the device from its holder. As mobile devices are beginning to include peripherals (for example, Bluetooth headsets and watches connected to a mobile phone stored in the pocket), these results may help guide interface designers with respect to distributing functions across the body between peripherals.

We describe the activity recognition component of the Soldier Assist System (SAS), which was built to meet the goals of DARPA’s Advanced Soldier Sensor Information System and Technology (ASSIST) program. As a whole, SAS provides an integrated solution that includes on-body data capture, automatic recognition of soldier activity, and a mul timedia interface that combines data search and exploration. The recognition component analyzes readings from six on-body accelerometers to identify activity. The activities are modeled by boosted 1D classifiers, which allows efficient selection of the most useful features within the learning algorithm. We present empirical results based on data collected at Georgia Tech and at the Army’s Aberdeen Proving Grounds during official testing by a DARPA appointed NIST evaluation team. Our approach achieves 78.7% for continuous event recognition and 70.3% frame level accuracy. The accuracy increases to 90.3% and 90.3% respectively when considering only the modeled activities. In addition to standard error metrics, we discuss error division diagrams (EDDs) for several Aberdeen data sequences to provide a rich visual representation of the performance of our system.

Anytime, anywhere experience capture is becoming the norm, especially capture involving multimedia. What's the future of mobile experience-capture technology? For example, if a company were to make a small, white, beautifully designed capture device with an “i” in front of its name, what would it be? Let's explore this question by examining three technologies: one that's currently available, one that could be here tomorrow, and one that could be a product in the future.

As mobile technology becomes a more integral part of our everyday lives, understanding the impact of different displays on perceived ease of use and overall performance is becoming increasingly important. In this paper, we evaluate three mobile displays: the MicroOptical SV-3, the Sony Librié, and the OQO Model 01. These displays each use different underlying technologies and offer unique features which could impact mobile use. The OQO is a hand-held device that utilizes a traditional transflective liquid crystal display (LCD). The MicroOptical SV-3 is a head-mounted display that uses a miniature LCD and offers hands free use. Finally, the Librié uses a novel, low power reflective electronic ink technology. We present a controlled 15-participant evaluation to assess the effectiveness of using these displays for reading while in motion.

In this paper we present an augmentation to the wear able computers typically used to determine if a patient is a candidate for surgery to correct problems associated with Gastroesophageal Reflux Disease (GERD). A wear able camera was used by the first author while participat ing in a 24–hour stomach acid pH study. After the study’s conclusion, an examination of the captured video and pH record revealed some results that allowed the first author to avoid many of the activities that result in symptoms related to GERD.

In this paper, we explore the concept of dual–purpose speech: speech that is socially appropriate in the context of a human–to–human conversation which also provides meaningful input to a computer. We motivate the use of dual–purpose speech and explore issues of privacy and technological challenges related to mobile speech recognition. We present three applications that utilize dual–purpose speech to assist a user in conversational tasks: the Calendar Navigator Agent, DialogTabs, and Speech Courier. The Calendar Navigator Agent navigates a user’s calendar based on socially appropriate speech used while scheduling appointments. DialogTabs allows a user to postpone cognitive processing of conversational material by proving short–term capture of transient in formation. Finally, Speech Courier allows asynchronous delivery of relevant conversational information to a third party.

Wearable computers have the potential to act as intelligent agents in everyday life and assist the user in a variety of tasks, using context to determine how to act. Location is the most common form of context used by these agents to determine the user's task. However, another potential use of location context is the creation of a predictive model of the user's future movements. We present a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales. These locations are then incorporated into a Markov model that can be consulted for use with a variety of applications in both single{user and collaborative scenarios.

Wearable computers have the potential to act as intelligent agents 	in everyday life and assist the user in a variety of tasks, using 	context to determine how to act.  Location is the most common form 	of context used by these agents to determine the user's task.  	However, another potential use of location context is the creation 	of a predictive model of the user's future movements.  We present a 	system that automatically clusters GPS data taken over an extended 	period of time into meaningful locations at multiple scales.  These 	locations are then incorporated into a Markov model that can be 	consulted for use with a variety of applications in both 	single-user and collaborative scenarios. 

The transferal of the desktop interface to the world at large is not the goal of ubiquitous computing. Rather, ubiquitous computing strives to increase the responsiveness of the world at large to the individual. A large part of this responsiveness is improved communication with other individuals. In this paper we describe a system that can enable ad–hoc collaboration between several people by creating a model of the daily schedules of individuals and by performing predictions based on this model. Using GPS data we learn to distinguish locations and track the times that these locations are visited. In addition, we use Markov models to predict which locations might be visited next based on the user's previous behavior.

The Gesture Pendant, a computer vision system worn as a piece of jewelry, allows the wearer to control electronic devices in the environment through simple hand gestures. Gestures provide an advantage over traditional device interfaces (such as remote controls) in that they are easily used by all people, including those with such disabilities as loss of vision, motor skills, and mobility. The Gesture Pendant can also be used for medical monitoring—as the user makes gestures, the pendant can analyze the movement of the hands to detect certain tremors, the frequency of which can indicate the progress of some diseases such as Parkinson’s.

In this paper we present a wearable device for control of home automation systems via hand gestures. This solution has many advantages over traditional home automation interfaces in that it can be used by those with loss of vision, motor skills, and mobility. By combining other sources of context with the pendant we can reduce the number and complexity of gestures while maintaining functionality. As users input gestures, the system can also analyze their movements for pathological tremors. This information can then be used for medical diagnosis, therapy, and emergency services.Currently, the Gesture Pendant can recognize control gestures with an accuracy of 95% and userdefined gestures with an accuracy of 97% It can detect tremors above 2Hz within +/-.1 Hz.