Fieldward and Pathward

Fieldward and Pathward are techniques for human-computer collaboration in designing user-defined gesture vocabularies on mobile devices. The user can interactively explore the continuous negative space of machine-recognisable but unused gestures with active feedback and feedforward provided by the system.  This enables users to freely create their own idiosyncratic gesture commands while maintaining adequate distance between gestures as defined by the gesture-recognition engine in use.


Joseph Malloch, Carla F. Griggio, Joanna McGrenere, and Wendy E. Mackay. 2017. Fieldward and Pathward: Dynamic Guides for Defining Your Own Gestures. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ’17). ACM, New York, NY, USA, 4266-4277. DOI:

Abstract: Although users accomplish ever more tasks on touch-enabled mobile devices, gesture-based interaction remains limited and almost never customizable by users. Our goal is to help users create gestures that are both personally memorable and reliably recognized by a touch-enabled mobile device. We address these competing requirements with two dynamic guides that use progressive feedforward to interactively visualize the “negative space” of unused gestures: the Pathward technique suggests four possible completions to the current gesture, and the Fieldward technique uses color gradients to reveal optimal directions for creating recognizable gestures. We ran a two-part experiment in which 27 participants each created 42 personal gesture shortcuts on a smartphone, using Pathward, Fieldward or No Feedforward. The Fieldward technique best supported the most common user strategy, i.e. to create a memorable gesture first and then adapt it to be recognized by the system. Users preferred the Fieldward technique to Pathward or No Feedforward, and remembered gestures more easily when using the technique. Dynamic guides can help developers design novel gesture vocabularies and support users as they design custom gestures for mobile applications.

Expressive Keyboards

The Expressive Keyboards project demonstrates and tests an approach to text input that celebrates input variation—rather than discarding it as noise—and maps it to continuous properties of the output.


Jessalyn Alvina, Joseph Malloch, and Wendy E. Mackay. 2016. Expressive Keyboards: Enriching Gesture-Typing on Mobile Devices. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST ’16). ACM, New York, NY, USA, 583-593. DOI:


Gesture-typing is an efficient, easy-to-learn, and error tolerant technique for entering text on software keyboards. Our goal is to “recycle” users’ otherwise-unused gesture variation to create rich output under the users’ control, without sacrificing accuracy. Experiment 1 reveals a high level of existing gesture variation, even for accurate text, and shows that users can consciously vary their gestures under different conditions. We designed an Expressive Keyboard for a smart phone which maps input gesture features identified in Experiment 1 to a continuous output parameter space, i.e. RGB color. Experiment 2 shows that users can consciously modify their gestures, while retaining accuracy, to generate specific colors as they gesture-type. Users are more successful when they focus on output characteristics (such as red) rather than input characteristics (such as curviness). We designed an app with a dynamic font engine that continuously interpolates between several typefaces, as well as controlling weight and random variation. Experiment 3 shows that, in the context of a more ecologically-valid conversation task, users enjoy generating multiple forms of rich output. We conclude with suggestions for how the Expressive Keyboard approach can enhance a wide variety of gesture recognition applications.