Evidence map›Paper›PMID 40012794›Full record

ArticleExtended abstracts on Human factors in computing systems. CHI Conference2023

Is cartoonized life-vlogging the key to increasing adoption of activity-oriented wearable camera systems?

Glenn Fernandes, Helen Zhu, Mahdi Pedram, Jacob Schauer, Soroush Shahi, Christopher Romano, Darren Gergle, Nabil Alshurafa

Abstract read
In one paragraph

Article in Extended abstracts on Human factors in computing systems. CHI Conference, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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
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

8 authors.

Glenn FernandesNorthwestern University Chicago, Illinois, USA.
Helen ZhuNorthwestern University Chicago, Illinois, USA.
Mahdi PedramNorthwestern University Chicago, Illinois, USA.
Jacob SchauerNorthwestern University Chicago, Illinois, USA.
Soroush ShahiNorthwestern University Chicago, Illinois, USA.
Christopher RomanoNorthwestern University Chicago, Illinois, USA.
Darren GergleNorthwestern University Chicago, Illinois, USA.
Nabil AlshurafaNorthwestern University Chicago, Illinois, USA.

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
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 R01 DK129843NIDDK NIH HHS R03 DK127128
6 · The paper itself

Abstract

Health science researchers studying human behavior rely on wearable cameras to visually confirm behaviors in real-world settings. However, privacy concerns significantly impede their adoption. Lens orientation and activity-oriented cameras have potential in balancing the need to visually validate the wearers' activities while reducing privacy concerns. To increase adoption and further alleviate privacy concerns while maintaining utility, generative stylizing approaches, like cartooning using generative adversarial networks (GANs), have recently shown promise. We investigate different cartoon-based obfuscation of activity-oriented footage through two studies. The first deploys crowdsourcing methods (n=60), while the second is experiential, where participants (n=49) don the device for an entire day and report concerns on their footage. Our findings support that cartoonization of activity-oriented data significantly reduces privacy concerns, particularly among bystanders in high privacy-concerning scenarios, while maintaining context verification (90% of participants). Through thematic analysis, we provide further insight for the community on best practices for cartoonization of activity-oriented videos.

Indexed as

activity-oriented wearable camerasComputing methodologiesCrowdsourcingEmpirical studies in HCIgenerative adversarial networksHuman-centered computingInformation systemsobfuscation techniquesprivacy concernsqualtrics surveyScene understanding

Identifiers

PMID40012794
PMCPMC11864368

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.