CoChat
Sign Up
Cited by 63Context-Aware Activity Recognition SystemsNon-Invasive Vital Sign MonitoringInnovation in Digital Healthcare Systems

iHear Food: Eating Detection Using Commodity Bluetooth Headsets

Gao, Yang, Zhang, Ning, Wang, Honghao, Ding, Xiang, Ye, Xu, Chen, Guanling, Cao, Yu·2016·2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
DOI 10.1109/chase.2016.14
Open this paper in CoChat — free account

Abstract

Monitoring a person's dietary behavior has many health applications (e.g. weight management). Existing approaches for automatic eating detection using wearable sensors often require custom hardware that may not be practical as their usability and energy efficiency have not been validated. In this paper, we propose to use off-the-shelf Bluetooth headsets to unobtrusively monitor and detect users' eating episodes by analyzing the chewing sound. The challenges of using commodity acoustic hardware include the limited sampling rate that reduces feature fidelity and the impact of environment noise in real-world settings. Our experimental results show that the traditional kernel-based approach using Support Vector Machine (SVM) could achieve 94-95% classification accuracy in lab settings, though the detection performance quickly degraded to 65-76% for in-the-field testing. We then propose a novel Deep Learning based classification technique that drastically improved detection accuracy to 77-94% despite the existence of ambient noise. To the best of our knowledge, this is the first study to use Bluetooth headsets to detect eating episodes and to use Deep Learning to significantly increase classification performance in this context. We believe that the adoption of readily available and low-cost consumer devices provides a foundation for practical deployment of automated dietary monitoring applications.

Integrity signals

Registry

Crossref · 10.1109/chase.2016.14

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The DBM-DNN approach achieved 94% accuracy, significantly better than SVM's 75.6%.

    “The accuracy of the proposed DBM-DNN approach is 94%, which is significantly better than SVM with a 75.6% accuracy.”

    From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [160]

  • An unsupervised five-layer DBM-DNN applied to eating episode detection via bluetooth headsets demonstrated classification improvement even in ambient noise.

    “In [160], an unsupervised five-layer DBM-DNN is applied for the automatic detection of eating episodes via commercial bluetooth headsets collecting raw audio signals, and demonstrate classification improvement even in the presence of ambient noise.”

    From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [160]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Gao, Y., Zhang, N., Wang, H., Ding, X., Ye, X., Chen, G., & Cao, Y.
Affiliations
University of Massachusetts Lowell (US)
ORCID
Venue
2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
Publisher
IEEE
Published
2016-06
Pages
163-172
ISSN
2832-2967 (ISSN-L)
DOI
10.1109/chase.2016.14
OpenAlex
W2516020480
Cited by
63
References
48
Citations by year
2026: 3 · 2025: 4 · 2024: 7 · 2023: 1 · 2022: 17 · 2021: 6 · 2020: 14 · 2019: 5 · 2018: 2 · 2017: 4
FWCI
1.27
Type
conference paper
Language
en
Open access
closed
Locations
IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (publishedVersion)
Funders
National Science Foundation
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
Keywords
eating detection, Bluetooth headset, dietary monitoring, ambient noise robustness, low-cost wearable sensors, deep learning classification, support vector machine, non-intrusive monitoring, weight management

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review