TY - GEN
T1 - S-ADL
T2 - 2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024
AU - Lee, Hansoo
AU - Kim, Auk
AU - Bae, Sang Won
AU - Lee, Uichin
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s)
PY - 2024/5/11
Y1 - 2024/5/11
N2 - In public health and safety, precise detection of blood alcohol concentration (BAC) plays a critical role in implementing responsive interventions that can save lives. While previous research has primarily focused on computer-based or neuropsychological tests for BAC identification, the potential use of daily smartphone activities for BAC detection in real-life scenarios remains largely unexplored. Drawing inspiration from Instrumental Activities of Daily Living (I-ADL), our hypothesis suggests that Smartphone-based Activities of Daily Living (S-ADL) can serve as a viable method for identifying BAC. In our proof-of-concept study, we propose, design, and assess the feasibility of using S-ADLs to detect BAC in a scenario-based controlled laboratory experiment involving 40 young adults. In this study, we identify key S-ADL metrics, such as delayed texting in SMS, site searching, and finance management, that significantly contribute to BAC detection (with an AUC-ROC and accuracy of 81%). We further discuss potential real-life applications of the proposed BAC model.
AB - In public health and safety, precise detection of blood alcohol concentration (BAC) plays a critical role in implementing responsive interventions that can save lives. While previous research has primarily focused on computer-based or neuropsychological tests for BAC identification, the potential use of daily smartphone activities for BAC detection in real-life scenarios remains largely unexplored. Drawing inspiration from Instrumental Activities of Daily Living (I-ADL), our hypothesis suggests that Smartphone-based Activities of Daily Living (S-ADL) can serve as a viable method for identifying BAC. In our proof-of-concept study, we propose, design, and assess the feasibility of using S-ADLs to detect BAC in a scenario-based controlled laboratory experiment involving 40 young adults. In this study, we identify key S-ADL metrics, such as delayed texting in SMS, site searching, and finance management, that significantly contribute to BAC detection (with an AUC-ROC and accuracy of 81%). We further discuss potential real-life applications of the proposed BAC model.
KW - Activities of daily living
KW - Alcohol drinking detection
KW - Functional assessment
KW - Machine learning
KW - Smartphone app usage
UR - http://www.scopus.com/inward/record.url?scp=85194836737&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85194836737&partnerID=8YFLogxK
U2 - 10.1145/3613904.3642832
DO - 10.1145/3613904.3642832
M3 - Conference contribution
AN - SCOPUS:85194836737
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems
Y2 - 11 May 2024 through 16 May 2024
ER -