Abstract: The use of hand gestures has a potential as a promising input metaphor. Wearables like smart textile and data gloves can provide hand gesture recognition to potentially replace, augment or improve existing input methods. Although recent bikes provide advanced functions with electro-mechanical components, the input metaphor still relies on mechanical switches or levers. In this paper, we investigate the acceptance and performance of using hand gesture during cycling. Through an observational study with 16 users, we devised a taxonomy of hand gestures. Users prefer subtle micro hand gestures to ensure safe cycling while maintaining a flexible controllability. We also implemented a wearable prototype that recognizes these gestures. In our evaluation, the prototype shows an average of 92 % accuracy while showing similar response time to existing mechanical inputs.
Yanke Tan, Sang Ho Yoon, Karthik Ramani
BikeGesture: User Elicitation and Performance of Micro Hand Gesture as Input for Cycling
In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA' 17: 2147-2154), Denver, CO, May 6-11, 2017