RecHap: An Interactive Recommender System for Navigating a Large Number of Mid-Air Haptic Designs
Karthikan Theivendran, Andy Wu, William Frier, Oliver Schneider
Abstract
Designing haptics is a difficult task especially when the user attempts to design a sensation from scratch. In the fields of visual and audio design, designers often use a large library of examples for inspiration, supported by intelligent systems like recommender systems. In this work, we contribute a corpus of 10 000 mid-air haptic designs (500 hand-designed sensations augmented 20x to create 10 000), and we use it to investigate a novel method for both novice and experienced hapticians to use these examples in mid-air haptic design. The RecHap design tool uses a neural-network based recommendation system that suggests pre-existing examples by sampling various regions of an encoded latent space. The tool also provides a graphical user interface for designers to visualize the sensation in 3D view, select previous designs, and bookmark favourites, all while feeling designs in real-time. We conducted a user study with 12 participants suggesting that the tool enables people to quickly explore design ideas and experience them immediately. The design suggestions encouraged collaboration, expression, exploration, and enjoyment, which improved creativity support.