Evidence map›Paper›PMID 38715730›Full record

ArticlePNAS nexus2024

A machine-learning-enabled smart neckband for monitoring dietary intake.

Taewoong Park, Talha Ibn Mahmud, Junsang Lee, Seokkyoon Hong, Jae Young Park, Yuhyun Ji, Taehoo Chang, Jonghun Yi, Min Ku Kim, Rita R Patel and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Photonic Nanomaterials for Wearable Health Solutions.Advanced materials (Deerfield Beach, Fla.) · 2025
    Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Taewoong ParkWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Talha Ibn MahmudElmore Family School of Electrical and Computer Engineering, West Lafayette, IN 47907, USA.ORCID https://orcid.org/0000-0002-6684-4228
Junsang LeeWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Seokkyoon HongWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID https://orcid.org/0009-0007-6540-0389
Jae Young ParkWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID https://orcid.org/0000-0003-3768-6889
Yuhyun JiWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Taehoo ChangSchool of Materials Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID https://orcid.org/0000-0002-3905-8301
Jonghun YiSchool of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea.
Min Ku KimWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Rita R PatelDepartment of Speech, Language and Hearing Sciences, Indiana University, Bloomington, IN 47408, USA.ORCID https://orcid.org/0000-0002-5354-5210
Dong Rip KimSchool of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea.ORCID https://orcid.org/0000-0001-6398-9483
Young L KimWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID https://orcid.org/0000-0003-3796-9643
Hyowon LeeWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID https://orcid.org/0000-0001-7628-1441
Fengqing ZhuElmore Family School of Electrical and Computer Engineering, West Lafayette, IN 47907, USA.ORCID https://orcid.org/0000-0002-3863-3220
Chi Hwan LeeWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID https://orcid.org/0000-0002-4868-7054

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing need for precise dietary monitoring across various health scenarios has led to innovations in wearable sensing technologies. However, continuously tracking food and fluid intake during daily activities can be complex. In this study, we present a machine-learning-powered smart neckband that features wireless connectivity and a comfortable, foldable design. Initially considered beneficial for managing conditions such as diabetes and obesity by facilitating dietary control, the device's utility extends beyond these applications. It has proved to be valuable for sports enthusiasts, individuals focused on diet control, and general health monitoring. Its wireless connectivity, ergonomic design, and advanced classification capabilities offer a promising solution for overcoming the limitations of traditional dietary tracking methods, highlighting its potential in personalized healthcare and wellness strategies.

Indexed as

bioelectronicsdietary intakemachine learningsmart neckbandwearable

Identifiers

PMID38715730
PMCPMC11075646

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.