SynchroWatch

Author:

Reyes Gabriel1,Wu Jason1,Juneja Nikita1,Goldshtein Maxim2,Edwards W. Keith1,Abowd Gregory D.1,Starner Thad1

Affiliation:

1. School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA

2. School of Aerospace Engineering, Georgia Institute of Technology, USA

Abstract

SynchroWatch is a one-handed interaction technique for smartwatches that uses rhythmic correlation between a user's thumb movement and on-screen blinking controls. Our technique uses magnetic sensing to track the synchronous extension and reposition of the thumb, augmented with a passive magnetic ring. The system measures the relative changes in the magnetic field induced by the required thumb movement and uses a time-shifted correlation approach with a reference waveform for detection of synchrony. We evaluated the technique during three distraction tasks with varying degrees of hand and finger movement: active walking, browsing on a computer, and relaxing while watching online videos. Our initial offline results suggest that intentional synchronous gestures can be distinguished from other movement. A second evaluation using a live implementation of the system running on a smartwatch suggests that this technique is viable for gestures used to respond to notifications or issue commands. Finally, we present three demonstration applications that highlight the technique running in real-time on the smartwatch.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Reference44 articles.

Cited by 35 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Around-device finger input on commodity smartwatches with learning guidance through discoverability;International Journal of Human-Computer Studies;2023-11

2. Robust Finger Interactions with COTS Smartwatches via Unsupervised Siamese Adaptation;Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology;2023-10-29

3. Headar;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2023-09-27

4. UnifiedSense: Enabling Without-Device Gesture Interactions Using Over-the-shoulder Training Between Redundant Wearable Sensors;Proceedings of the ACM on Human-Computer Interaction;2023-09-11

5. GloveSense: A Hand Gesture Recognition System Based on Inductive Sensing;IEEE Sensors Journal;2023-05-01

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