Peter Kun

Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, from enhancing the user experience in mobile apps to enabling appropriate interventions in digital health apps. Towards this goal, previous studies have relied on datasets combining passive sensor data with human-provided annotations or self-reports. However, many existing datasets are limited in scope, often focusing on specific countries primarily in the Global North, involving a small number of participants, or using a limited range of pre-processed sensors. These limitations restrict the ability to capture cross-country variations of human behavior, including the possibility of studying model generalization, and robustness. To address this gap, we introduce DiversityOne, a dataset which spans eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the United Kingdom) and includes data from 782 college students over four weeks. DiversityOne contains data from 26 smartphone sensor modalities and 350K+ self-reports. As of today, it is one of the largest and most diverse publicly available datasets, while featuring extensive demographic and psychosocial survey data. DiversityOne opens the possibility of studying important research problems in ubiquitous computing, particularly in domain adaptation and generalization across countries, all research areas so far largely underexplored because of the lack of adequate datasets.

This workshop explores the transformative potential of generative artificial intelligence (GenAI) in design research. GenAI, capable of creating new content such as images, text, music, video, and code, raises important questions about authorship, agency, and design practice. Inspired by Roland Barthes’ "The Death of the Author," this workshop examines how GenAI reshapes design research roles and methods. Key topics include best practices, ethical considerations, knowledge generation, and collaboration patterns between human and AI creatives. Building on themes identified in the successful DIS 2023 workshop, this 2-day event invites designers and researchers to present completed projects, works-in-progress, and theoretical provocations. The structure allows time for both presentations and in-depth discussions, aiming to develop an online resource library and a collaborative publication. The workshop seeks to advance the discourse on GenAI, addressing its challenges and opportunities in design research.

GenFrame is an interactive picture frame that mimics traditional paintings while being equipped with generative AI capabilities. Painting “portraits of a girl” has been a trope in art history for centuries. However, in the era of generative AI models, the permanence of traditional art is challenged. When an AI model is able to mimic any painting style, it can also give agency to the viewer to repaint the painting per individual desires. In this installation, we modify the role of the museum placard to provide a minimal tangible interface to change the style of the image and the mood of the depicted girl. In this way, the generative AI model is interfaced in tangible ways, instead of the regular prompt paradigm. Demo video available here: https://youtu.be/ga7pFgAOPiY

This one-day workshop seeks to bring together design researchers that have different orientations and practices for futures and futuring to unpack the role of futuring across design, design artefacts, and designers. Our goal in this workshop is to use examples of established futures to engage with and reflect on questions around the role of futures in HCI/design. We believe that there is a common thread between work from many different researchers, but that the people doing that work are spread across different disciplines and geographical locations. To that end, this workshop will assemble researchers working with different methods and approaches to futures in design and will aim to identify core challenges and opportunities for futures in HCI.

Image-generation AI models have triggered a paradigm shift in how we can express ourselves in visual art. Despite their widespread use in a short amount of time, embedding these models into interactive artifacts is still largely unexplored. In this pictorial, we unpack the design and development process of GenFrame, an image generating picture frame that utilizes generative AI capabilities to mimic traditional paintings. Our work details the necessary steps to integrate generative AI into interactive artifacts and highlights important design considerations for controlling image-generation models in order to achieve specific design intents. Our insights provide interaction designers with a more comprehensive understanding and approach towards utilizing image-generation AI models for interactive artifacts. A demo can be viewed at https://youtu.be/1rhW4fazaBY

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.

Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health apps. However, even though model generalization issues have been highlighted in many studies, the focus has always been on improving the accuracies of models using different sensing modalities and machine learning techniques, with datasets collected in homogeneous populations. In contrast, less attention has been given to studying the performance of mood inference models to assess whether models generalize to new countries. In this study, we collected a mobile sensing dataset with 329K self-reports from 678 participants in eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, UK) to assess the effect of geographical diversity on mood inference models. We define and evaluate country-specific (trained and tested within a country), continent-specific (trained and tested within a continent), country-agnostic (tested on a country not seen on training data), and multi-country (trained and tested with multiple countries) approaches trained on sensor data for two mood inference tasks with population-level (non-personalized) and hybrid (partially personalized) models. We show that partially personalized country-specific models perform the best yielding area under the receiver operating characteristic curve (AUROC) scores of the range 0.78--0.98 for two-class (negative vs. positive valence) and 0.76--0.94 for three-class (negative vs. neutral vs. positive valence) inference. Further, with the country-agnostic approach, we show that models do not perform well compared to country-specific settings, even when models are partially personalized. We also show that continent-specific models outperform multi-country models in the case of Europe. Overall, we uncover generalization issues of mood inference models to new countries and how the geographical similarity of countries might impact mood inference.