BudsID: Mobile-Ready and Expressive Finger Identification Input for Earbuds

Jiwan Kim, Mingyu Han, and Ian Oakley
CHI '25: Proceedings of the 2025 CHI conference on Human Factors in Computing systems.
DOI: https://dl.acm.org/doi/10.1145/3706598.3714133
Open Source: https://github.com/witlab-kaist/BudsID
Session: Earables and Hearable

Abstract

Wireless earbuds are an appealing platform for wearable computing on-the-go. However, their small size and out-of-view location mean they support limited different inputs. We propose finger identification input on earbuds as a novel technique to resolve these problems. This technique involves associating touches by different fingers with different responses. To enable it on earbuds, we adapted prior work on smartwatches to develop a wireless earbud featuring a magnetometer that detects fields from a magnetic ring. A first study reveals participants achieve rapid, precise earbud touches with different fingers, even while mobile (time: 0.98s, errors: 5.6%). Furthermore, touching fingers can be accurately classified (96.9%). A second study shows strong performance with a more expressive technique involving multi-finger double-taps (inter-touch time: 0.39s, errors: 2.8%) while maintaining high accuracy (94.7%). We close by exploring and evaluating the design of earbud finger identification applications and demonstrating the feasibility of our system on low-resource devices.

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