Mobile phone user interfaces typically show an icon to indicate remaining battery, but not the amount of time the device can be used for, often forcing users to make faulty estimates and predictions about battery life. Here we report on two studies that capture users' experiences with a user-centered battery interface design. In Study 1, we analyze 12 participants' use of mobile phones, demonstrating that mobile phone users do not know how or what to do to extend their mobile's battery life. We further identify the information they rely on to assess battery life. In Study 2, we use this information to design, prototype and evaluate an interactive battery interface (IBI) with another 22 participants. Our findings describe how users perceive battery life and how we used their mental models of mobile phone batteries to create IBI. Lastly, we report on the users' experiences and IBI's effect on battery lifetime, showing gains of approximately 27% over the course of a day.

With a focus on presenting information at the right time, the ubicomp community can benefit greatly from learning the most salient human measures of cognitive load. Cognitive load can be used as a metric to determine when or whether to interrupt a user. In this paper, we collected data from multiple sensors and compared their ability to assess cognitive load. Our focus is on visual perception and cognitive speed-focused tasks that leverage cognitive abilities common in ubicomp applications. We found that across all participants, the electrocardiogram median absolute deviation and median heat flux measurements were the most accurate at distinguishing between low and high levels of cognitive load, providing a classification accuracy of over 80% when used together. Our contribution is a real-time, objective, and generalizable method for assessing cognitive load in cognitive tasks commonly found in ubicomp systems and situations of divided attention.

Automatic recognition of activities using time series data collected from exercise can facilitate development of applications that motivate people to exercise more frequently and actively. This article presents a method for recognizing nine different everyday sport activities, such as running, walking, aerobics and Nordic walking, using only two-dimensional wrist-worn accelerometer. The suggested method is based on clustering the data by first using an EM-algorithm to form homogeneous groups and then applying C4.5-based decision trees inside these groups. The features extracted for classification process are simple features, such as variance and mean, which are calculated from compressed signals that contain only such points of the original time series where the derivative is equal to zero. The data were collected by ten subjects and they contained nine different sports. Using the presented method, the data were classified with an accuracy of 85%, whereas the accuracy using an automatically generated decision tree was 80%. The purpose of this method is to recognize activities in order to form an activity diary.

This article presents an approach to estimating exercise energy expenditure based on acceleration measurements from a wrist-worn biaxial sensor. The method uses the linear mixed model that makes it possible to model both between-subject and within-subject variation in energy expenditure. More precisely, a random-intercepts model is used. The variance and mean of the acceleration signals at 15-second intervals as well as subject demographics (height, weight, body mass index, age and VO2max) are used. Energy expenditure is modelled in four different activities: walking, running, Nordic walking and bicycling. This study introduces an effective backward model selection procedure for selecting the fixed-effect variables in the model. The procedure uses leave-one-out cross-validation to be able to effectively exploit the available data set and to ensure the robustness of the model. Estimation accuracy in test sets is used as a criterion of model performance. The model selection procedure proposed notably improves estimation accuracy. In walking, running, Nordic walking and bicycling, average estimation errors of 3.9, 3.6, 1.9 and13.5 percent are reached. The respective Pearson correlations for these activities are 0.91, 0.98, 0.97, and 0.81. These results are also compared to the performance of the general linear model. It is discovered that the linear mixed model outperforms the model that does not take the individual levels of energy expenditure of the subjects into account.