The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers
Abstract
Smartwatches and wearables are unique in that they reside on the body, presenting great potential for always-available input and interaction. Their position on the wrist makes them ideal for capturing bio-acoustic signals. We developed a custom smartwatch kernel that boosts the sampling rate of a smartwatch's existing accelerometer to 4 kHz. Using this new source of high-fidelity data, we uncovered a wide range of applications. For example, we can use bio-acoustic data to classify hand gestures such as flicks, claps, scratches, and taps, which combine with on-device motion tracking to create a wide range of expressive input modalities. Bio-acoustic sensing can also detect the vibrations of grasped mechanical or motor-powered objects, enabling passive object recognition that can augment everyday experiences with context-aware functionality. Finally, we can generate structured vibrations using a transducer, and show that data can be transmitted through the human body. Overall, our contributions unlock user interface techniques that previously relied on special-purpose and/or cumbersome instrumentation, making such interactions considerably more feasible for inclusion in future consumer devices.
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Cites
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Cited by
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
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“For example, the projects presented in [161, 162] utilize sampling rates above the typical rate.”
BackgroundSensing Fine-Grained Hand Activity with Smartwatches
Laput, G., & Harrison, C. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300568 · Cited by 147
Self-citationToward fairness in AI for people with disabilities SBG@a research roadmap
Guo, A., Kamar, E., Vaughan, J. W., Wallach, H., & Morris, M. R. (2020) · ACM SIGACCESS Accessibility and Computing · doi:10.1145/3386296.3386298 · Cited by 128
FingerPing: Recognizing Fine-grained Hand Poses using Active Acoustic On-body Sensing
Zhang, C., Xue, Q., Waghmare, A., Meng, R., Jain, S., Han, Y., Li, X., Cunefare, K., Ploetz, T., Starner, T., et al. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174011 · Cited by 82
LimbMotion: Decimeter-level Limb Tracking for Wearable-based Human-Computer Interaction
Zhou, H., Gao, Y., Song, X., Liu, W., & Dong, W. (2019) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3369836 · Cited by 27
Metadata
- Authors
- Laput, G., Xiao, R., & Harrison, C.
- Venue
- Proceedings of the 29th Annual Symposium on User Interface Software and Technology
- Publisher
- ACM
- Published
- 2016-10-16
- Pages
- 321-333
- OpenAlex
- W2538172027
- Cited by
- 265
- References
- 45
- FWCI
- 10.10
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Laput, G., Xiao, R., & Harrison, C. (2016). ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers. Proceedings of the 29th Annual Symposium on User Interface Software and Technology. https://doi.org/10.1145/2984511.2984582
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