Recognizing Unintentional Touch on Interactive Tabletop

Abstract

A multi-touch interactive tabletop is designed to embody the benefits of a digital computer within the familiar surface of a physical tabletop. However, the nature of current multi-touch tabletops to detect and react to all forms of touch, including unintentional touches, impedes users from acting naturally on them. In our research, we leverage gaze direction, head orientation and screen contact data to identify and filter out unintentional touches, so that users can take full advantage of the physical properties of an interactive tabletop, e.g., resting hands or leaning on the tabletop during the interaction. To achieve this, we first conducted a user study to identify behavioral pattern differences (gaze, head and touch) between completing usual tasks on digital versus physical tabletops. We then compiled our findings into five types of spatiotemporal features, and train a machine learning model to recognize unintentional touches with an F1 score of 91.3%, outperforming the state-of-the-art model by 4.3%. Finally we evaluated our algorithm in a real-time filtering system. A user study shows that our algorithm is stable and the improved tabletop effectively screens out unintentional touches, and provide more relaxing and natural user experience. By linking their gaze and head behavior to their touch behavior, our work sheds light on the possibility of future tabletop technology to improve the understanding of users' input intention.

Model-driven development for interactive surface prototypes

“…14 and Fig. 15 present graphical model representations for the prototypes in [47] and [13]. Table 9 presents the overall results.…”

“…We then used the search strings to find publications in databases. tabletop interaction "interactive tabletop" [13,44,47] . .…”

“…The hardware components were then used to define stereotypes (column 2) from which Device metaclass (column 1) was derived. Depth Sensor [7, 10, 16, 29, 31, 38-40, 42, 46] Infrared Sensor [27,36] Self-built Sensor [24] Imaging Device Projector [7,11,15,16,27,29,31,32,36,37,[39][40][41][42]46] Display [11,41] Actuator Device Actuator [15,27,32,36,40,42] Human Interface Device Mobile Device [16,24,29,31,32,37,40,42] Peripheral Device Phicon [36] Marker [24,27,32,47] Infrared lamp [27,36,37] Mirror [11,27,36] Audio Device Transducer [11]…”

“…Despite the different approaches, existing implementations suggest common underlying configurations. Toolkits in the research space of interactive surface environments [18,22] simplify prototyping interactive surfaces [15,42,47]. However, the accelerated development of these technologies discontinues the usability of toolkits, in some cases making toolkits obsolete.…”

“…14. Validation based on Traces: Graphical model representation of prototype from Xu et al in[47]. 29 components have been identified and 29 components have been modeled.…”