TY - GEN
T1 - Is cartoonized life-vlogging the key to increasing adoption of activity-oriented wearable camera systems?
AU - Fernandes, Glenn
AU - Zhu, Helen
AU - Pedram, Mahdi
AU - Schauer, Jacob
AU - Shahi, Soroush
AU - Romano, Christopher
AU - Gergle, Darren
AU - Alshurafa, Nabil
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/4/19
Y1 - 2023/4/19
N2 - 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.
AB - 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.
KW - activity-oriented wearable cameras
KW - generative adversarial networks
KW - obfuscation techniques
KW - privacy concerns
KW - qualtrics survey
UR - http://www.scopus.com/inward/record.url?scp=85158075549&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85158075549&partnerID=8YFLogxK
U2 - 10.1145/3544549.3585812
DO - 10.1145/3544549.3585812
M3 - Conference contribution
AN - SCOPUS:85158075549
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2023 - Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
Y2 - 23 April 2023 through 28 April 2023
ER -