Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK
The paper titled “Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK”, a collaboration between 7 institutes of the WeNet consortium and led by Idiap Research Institute, has been accepted at the ACM Conference on Human Factors in Computing Systems (CHI). ACM CHI is the top venue for publishing research in human-computer interaction. The conference is to be held in Hamburg, Germany, April 23-28, 2023.
Lakmal Meegahapola (Idiap Research Institute & EPFL), who co-authored the paper, will present it during the conference. Given below is the abstract of the paper:
Smartphones enable understanding human behavior with activity recognition to support people’s daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous populations within a country. However, people are more sedentary in the post-pandemic world with the prevalence of remote/hybrid work/study settings, making detecting simple activities less meaningful for context-aware applications. Hence, the understanding of (i) how multimodal smartphone sensors and machine learning models could be used to detect complex daily activities that can better inform about people’s daily lives, and (ii) how models generalize to unseen countries, is limited. We analyzed in-the-wild smartphone data and ∼216K self-reports from 637 college students in five countries (Italy, Mongolia, UK, Denmark, Paraguay). Then, we defined a 12-class complex daily activity recognition task and evaluated the performance with different approaches. We found that even though the generic multi-country approach provided an AUROC of 0.70, the country-specific approach performed better with AUROC scores in [0.79-0.89]. We believe that research along the lines of diversity awareness is fundamental for advancing human behavior understanding through smartphones and machine learning, for more real-world utility across countries.
Paper link: http://publications.idiap.ch/attachments/papers/2023/Assi_CHI23_2023.pdf