short-paper

Towards In-Ear Inertial Jaw Clenching Detection

Authors:

Siddharth Rupavatharam,

Marco GruteserAuthors Info & Claims

EarComp'19: Proceedings of the 1st International Workshop on Earable Computing

Pages 54 - 55

https://doi.org/10.1145/3345615.3361134

Published: 17 February 2020 Publication History

Get Access

Abstract

Bruxism is a jaw-muscle condition characterized by repetitive clenching or grinding of teeth. Existing methods of detecting jaw clenching towards diagnosing bruxism are either invasive or not very reliable. As a first step towards building a reliable, non-invasive and light weight bruxism detector, we propose an eSense based in-ear inertial jaw clenching detection technique that detects peaks/dips in gyroscope vector magnitude. We also present results from preliminary experiments that show an equal error rate of 1% when the person is stationary and 4% when moving.

References

[1]

F. Kawsar, C. Min, A. Mathur, and A. Montanari. Earables for personal-scale behavior analytics. IEEE Pervasive Computing, 17(3):83--89, 2018.

Digital Library

Google Scholar

[2]

F. Lobbezoo, J. Ahlberg, A. Glaros, T. Kato, K. Koyano, G. Lavigne, R. De Leeuw, D. Manfredini, P. Svensson, and E. Winocur. Bruxism defined and graded: an international consensus. Journal of oral rehabilitation, 40(1):2--4, 2013.

Crossref

Google Scholar

[3]

D. Manfredini and F. Lobbezoo. Relationship between bruxism and temporomandibular disorders: a systematic review of literature from 1998 to 2008. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology, 109(6):e26--e50, 2010.

Google Scholar

Cited By

View all

Zhang XWang YHan YLiang CChatterjee ITang JYi XPatel SShi Y(2024)The EarSAVAS DatasetProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596168:2(1-26)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659616
Montanari AFerlini ABalaji AMascolo CKawsar F(2023)EarSet: A Multi-Modal Dataset for Studying the Impact of Head and Facial Movements on In-Ear PPG SignalsScientific Data10.1038/s41597-023-02762-310:1Online publication date: 1-Dec-2023
https://doi.org/10.1038/s41597-023-02762-3
Röddiger TClarke CBreitling PSchneegans TZhao HGellersen HBeigl M(2022)Sensing with EarablesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35503146:3(1-57)Online publication date: 7-Sep-2022
https://dl.acm.org/doi/10.1145/3550314
Show More Cited By

Index Terms

Towards In-Ear Inertial Jaw Clenching Detection
1. Applied computing
2. Human-centered computing

Index terms have been assigned to the content through auto-classification.

Recommendations

Towards Respiration Rate Monitoring Using an In-Ear Headphone Inertial Measurement Unit
EarComp'19: Proceedings of the 1st International Workshop on Earable Computing

State-of-the-art respiration tracking devices require specialized equipment, making them impractical for every day at-home respiration sensing. In this paper, we present the first system for sensing respiratory rates using in-ear headphone inertial ...
PilotEar: Enabling In-ear Inertial Navigation
UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers

Navigation systems are used daily. While different types of navigation systems exist, inertial navigation systems (INS) have favorable properties for some wearables which, for battery and form factors may not be able to use GPS. Earables (aka ear-worn ...
Developing a portable gait cycle detection system using an inertial sensor and evaluating the accuracy of the gait cycle detection
Recent Innovations on Biomedical Engineering

Although researches had analyzed gait using small sensors, they analyzed only normal gaits. Thus, a research that can overcome the spatial limitations of the existing motion analyses and diagnose abnormal gaits for medical treatment is needed. Accordingly, ...

Comments

Information & Contributors

Information

Published In

EarComp'19: Proceedings of the 1st International Workshop on Earable Computing

September 2019

59 pages

ISBN:9781450369022

DOI:10.1145/3345615

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 February 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Short-paper
Research
Refereed limited

Conference

UbiComp '19

Sponsor:

SIGMOBILE
SIGCHI

UbiComp '19: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 9, 2019

London, United Kingdom

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
219
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)1

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhang XWang YHan YLiang CChatterjee ITang JYi XPatel SShi Y(2024)The EarSAVAS DatasetProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596168:2(1-26)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659616
Montanari AFerlini ABalaji AMascolo CKawsar F(2023)EarSet: A Multi-Modal Dataset for Studying the Impact of Head and Facial Movements on In-Ear PPG SignalsScientific Data10.1038/s41597-023-02762-310:1Online publication date: 1-Dec-2023
https://doi.org/10.1038/s41597-023-02762-3
Röddiger TClarke CBreitling PSchneegans TZhao HGellersen HBeigl M(2022)Sensing with EarablesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35503146:3(1-57)Online publication date: 7-Sep-2022
https://dl.acm.org/doi/10.1145/3550314
Gu XOu LOng DWang YMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)MM-ALTProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548411(3328-3337)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548411
Röddiger TDinse CBeigl M(2021)Wearability and Comfort of Earables During SleepProceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460421.3480432(150-152)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460421.3480432
Bondareva EHauksdóttir EMascolo C(2021)Earables for Detection of Bruxism: a Feasibility StudyAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479327(146-151)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3479327
Ahuja AFerlini AMascolo C(2021)PilotEar: Enabling In-ear Inertial NavigationAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479326(139-145)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3479326
Islam MHossain TAhad MInoue S(2020)Exploring Human Activities Using eSense Earable DeviceActivity and Behavior Computing10.1007/978-981-15-8944-7_11(169-185)Online publication date: 24-Dec-2020
https://doi.org/10.1007/978-981-15-8944-7_11

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Towards Respiration Rate Monitoring Using an In-Ear Headphone Inertial Measurement Unit

PilotEar: Enabling In-ear Inertial Navigation

Developing a portable gait cycle detection system using an inertial sensor and evaluating the accuracy of the gait cycle detection

Comments

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Conference

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Towards Respiration Rate Monitoring Using an In-Ear Headphone Inertial Measurement Unit

PilotEar: Enabling In-ear Inertial Navigation

Developing a portable gait cycle detection system using an inertial sensor and evaluating the accuracy of the gait cycle detection

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations