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We conducted a series of user studies to understand and clarify the fundamental characteristics of pressure in user interfaces for mobile devices. We seek to provide insight to clarify a longstanding discussion on mapping functions for pressure input. Previous literature is conflicted about the correct transfer function to optimize user performance. Our study results suggest that the discrepancy can be explained by different signal conditioning circuitry and with improved signal conditioning the user-performed precision relationship is linear. We also explore the effects of hand pose when applying pressure to a mobile device from the front, the back, or simultaneously from both sides in a pinching movement. Our results indicate that grasping type input outperforms single-sided input and is competitive with pressure input against solid surfaces. Finally we provide an initial exploration of non-visual multimodal feedback, motivated by the desire for eyes-free use of mobile devices. The findings suggest that non-visual pressure input can be executed without degradation in selection time but suffers from accuracy problems.

Activity recognition is a core component of many intelligent and context-aware systems. We present a solution for discreetly and unobtrusively recognizing common work activities above a work surface without using cameras. We demonstrate our approach, which utilizes an RF-radar sensor mounted under the work surface, in three domains: recognizing work activities at a convenience-store counter, recognizing common office deskwork activities, and estimating the position of customers in a showroom environment. Our examples illustrate potential benefits for both post-hoc business analytics and for real-time applications. Our solution was able to classify seven clerk activities with 94.9% accuracy using data collected in a lab environment and able to recognize six common deskwork activities collected in real offices with 95.3% accuracy. Using two sensors simultaneously, we demonstrate coarse position estimation around a large surface with 95.4% accuracy. We show that using multiple projections of RF signal leads to improved recognition accuracy. Finally, we show how smartwatches worn by users can be used to attribute an activity, recognized with the RF sensor, to a particular user in multi-user scenarios. We believe our solution can mitigate some of users’ privacy concerns associated with cameras and is useful for a wide range of intelligent systems.

Motivated by the addition of gyroscopes to a large number of new smart phones, we study the effects of combining accelerometer and gyroscope data on the recognition rate of motion gesture recognizers with dimensionality constraints. Using a large data set of motion gestures we analyze results for the following algorithms: Protractor3D, Dynamic Time Warping (DTW) and Regularized Logistic Regression (LR). We chose to study these algorithms because they are relatively easy to implement, thus well suited for rapid prototyping or early deployment during prototyping stages. For use in our analysis, we contribute a method to extend Protractor3D to work with the 6D data obtained by combining accelerometer and gyroscope data. Our results show that combining accelerometer and gyroscope data is beneficial also for algorithms with dimensionality constraints and improves the gesture recognition rate on our data set by up to 4%.