Evidence map›Paper›PMID 37179571›Full record

Article... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks2022

SmartAct: Energy Efficient and Real-Time Hand-to-Mouth Gesture Detection Using Wearable RGB-T.

Soroush Shahi, Mahdi Pedram, Glenn Fernandes, Nabil Alshurafa

Open access · greenAbstract read
In one paragraph

Article in ... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 48% of its field
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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. When2Trigger: Evaluation Trade-offs in Vision-based Real-Time Eating Detection Systems.... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks · 2024
    Article
  2. HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024
    Article
  3. Detecting Eating and Social Presence with All Day Wearable RGB-T....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2023
    Article
  4. SmokeMon: Unobtrusive Extraction of Smoking Topography Using Wearable Energy-Efficient Thermal.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022
    Article
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

4 authors at 1 institution in 1 country.

Soroush ShahiDepartment of Computer Science, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA.
Mahdi PedramDepartment of Computer Science, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA.
Glenn FernandesDepartment of Computer Science, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA.
Nabil AlshurafaDepartment of Computer Science, Department of Preventive Medicine, Northwestern University, Evanston, IL, USA.
Northwestern University · US

Funding

EAT: A Reliable Eating Assessment Technology for Free-living Individuals.R01DK129843 · NIDDK · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ALSHURAFA, NABIL · 2021 to 2025
$3.3M
SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing.K25DK113242 · NIDDK · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ALSHURAFA, NABIL · 2018 to 2022
$881k
BehaviorSight: Privacy enhancing wearable system to detect health risk behaviors in real-time.R21EB030305 · NIBIB · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ALSHURAFA, NABIL · 2020 to 2020
$607k
WildCam: A Privacy Conscious Wearable Eating Detection Camera People will Actually Wear in the WildR03DK127128 · NIDDK · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ALSHURAFA, NABIL · 2021 to 2022
$234k
NIBIB NIH HHS R21 EB030305NIDDK NIH HHS K25 DK113242NIDDK NIH HHS R01 DK129843NIDDK NIH HHS R03 DK127128
6 · The paper itself

Abstract

Researchers have been leveraging wearable cameras to both visually confirm and automatically detect individuals' eating habits. However, energy-intensive tasks such as continuously collecting and storing RGB images in memory, or running algorithms in real-time to automate detection of eating, greatly impacts battery life. Since eating moments are spread sparsely throughout the day, battery life can be mitigated by recording and processing data only when there is a high likelihood of eating. We present a framework comprising a golf-ball sized wearable device using a low-powered thermal sensor array and real-time activation algorithm that activates high-energy tasks when a hand-to-mouth gesture is confirmed by the thermal sensor array. The high-energy tasks tested are turning on the RGB camera (Trigger RGB mode) and running inference on an on-device machine learning model (Trigger ML mode). Our experimental setup involved the design of a wearable camera, 6 participants collecting 18 hours of data with and without eating, the implementation of a feeding gesture detection algorithm on-device, and measures of power saving using our activation method. Our activation algorithm demonstrates an average of at-least 31.5% increase in battery life time, with minimal drop of recall (5%) and without impacting the accuracy of detecting eating (a slight 4.1% increase in F1-Score).

Identifiers

PMID37179571
PMCPMC10176499
OpenAlexW4308086694

What OpenQuestion holds

Textmetadata
LicenceTDM
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.