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

2025-03-04

AI Summary

Wireless earbuds face severe input constraints due to their miniature size and covert wearability. This work proposes a finger-touch recognition technique leveraging an external magnetic ring and the earbud’s built-in magnetometer—enabling, for the first time, on-device lightweight, high-accuracy multi-finger identification and double-tap temporal modeling, while ensuring mobility robustness and efficient deployment on resource-constrained embedded platforms. Our method integrates magnetic induction sensing, temporal touch-feature extraction, and lightweight classification (e.g., SVM or decision trees), performing end-to-end edge processing directly on the earbud. Experiments demonstrate an average single-finger touch response latency of 0.98 s with a 5.6% error rate; double-tap inter-press interval estimation achieves only 2.8% error, and overall recognition accuracy exceeds 94.7%. This work establishes a novel interaction paradigm for wearable audio devices—natural, implicit, and highly robust.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Humans and AI: Interaction Techniques and Devices
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

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.

Problem

• Limited input options on wireless earbuds due to small size and out-of-view location.
• Developed finger identification input using a magnetometer-equipped earbud.
• Achieved rapid, precise, and accurate finger touch classification for mobile use.

Innovation

• Finger identification input on earbuds
• Magnetometer detects magnetic ring fields
• Multi-finger double-taps for expressive input