Abstract. Three tools for acquiring data about people, their behavior, and their use of technology in natural settings are described: (1) a context-aware experience sampling tool, (2) a ubiquitous sensing system that detects environmental changes, and (3) an image-based experience sampling system. We discuss how these tools provide researchers with a flexible toolkit for collecting data on activity in homes and workplaces, particularly when used in combination. We outline several ongoing studies to illustrate the versatility of these tools. Two of the tools are currently available to other researchers to use.

No abstract

A new software tool for user-interface development and assessment of ubiquitous computing applications is available for CHI researchers. The software permits researchers to use common PDA mobile computing devices for experience sampling studies. The basic tool offers options not currently available in any other open-source sampling package. However, the tool also has one a completely new type of functionality: context-aware experience sampling. This feature permits researchers to acquire feedback from users only in particular situations that are detected by sensors connected to a mobile computing device. KeywordsContext-aware, experience sampling, reflection, eliciting preferences, PDA, ubiquitous and mobile computing. THE PROBLEMUser needs are typically elicited via personal or focus group interviews, site visits, and photographic and video analysis. Often, however, users know more than they say in a single or even several interviews [1]. As user interface design moves off the desktop and into the real world, two new challenges for designers emerge: (1) developing realistic task specifications that respond to the complexity of fastchanging, real world activities, and (2) evaluating new technologies in realistic contexts. Desktop computing applications can be designed and evaluated using controlled, laboratory observation because most user interface design has nothing to do with physical space [2]. Developers of ubiquitous and mobile computing applications for the home and workplace, however, currently lack a powerful and economical assessment toolset that accounts for user activity in a broader context. The behavior of the people and their response to technology is critically dependent upon the environment and context in which information is presented or requested.The most popular assessment instruments in use today for studying the activities of people in natural settings are self report recall surveys, time diaries, direct field observation, and experience sampling. Self-report recall surveys suffer from recall and selective reporting biases -users can often not remember what they did. Time diaries, where users write down what they do during the day, are burdensome for the user. Although direct field observation can provide helpful qualitative and quantitative measures, it is costly, timeconsuming, and disruptive and therefore not practical for many design tasks. The experience sampling method (ESM) has been used primarily for time-use analysis [3] and only recently for interface design [4,5]. Subjects carry a beeper device that "samples" for information on some predetermined schedule. When the device beeps, subjects answer questions of interest to the researchers. With a sufficient number of subjects and samples, a statistical model of behavior can be generated. The ESM is less susceptible to subject recall errors than other self-report feedback elicitation methods [3], but it high sampling rates interrupt activities of interest and irritate subjects. Image-based experience sampling alleviates these ...

Ubiquitous, context-aware computer systems may ultimately enable computer applications that naturally and usefully respond to a user's everyday activity. Although new algorithms that can automatically detect context from wearable and environmental sensor systems show promise, many of the most flexible and robust systems use probabilistic detection algorithms that require extensive libraries of training data with labeled examples. In this paper, we describe the need for such training data and some challenges we have identified when trying to collect it while testing three contextdetection systems for ubiquitous computing and mobile applications.

Study Objectives Individuals with obstructive sleep apnea (OSA), characterized by frequent sleep disruptions from tongue muscle relaxation and airway blockage, are known to benefit from on-demand electrical stimulation of the hypoglossal nerve. Hypoglossal nerve stimulation (HNS) therapy, which activates the protrusor muscles of the tongue during inspiration, has been established in multiple clinical studies as safe and effective, but the mechanistic understanding for why some stimulation parameters work better than others has not been thoroughly investigated. Methods In this study, we developed a detailed biophysical model that can predict the spatial recruitment of hypoglossal nerve fascicles and axons within these fascicles during stimulation through nerve cuff electrodes. Using this model, three HNS programming scenarios were investigated including grouped cathode (---), single cathode (o-o), and guarded cathode bipolar (+-+) electrode configurations. Results Regardless of electrode configuration, nearly all hypoglossal nerve axons circumscribed by the nerve cuff were recruited for stimulation amplitudes <3 V. Within this range, monopolar configurations required lower stimulation amplitudes than the guarded bipolar configuration to elicit action potentials within hypoglossal nerve axons. Further, the spatial distribution of the activated axons was more uniform for monopolar versus guarded bipolar configurations. Conclusions The computational models predicted that monopolar HNS provided the lowest threshold and the least sensitivity to rotational angle of the nerve cuff around the hypoglossal nerve; however, this setting also increased the likelihood for current leakage outside the nerve cuff, which could potentially activate axons in unintended branches of the hypoglossal nerve. Clinical Trial Registration NCT01161420.