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

This Research through Design paper explores how object detection may be applied to a large digital art museum collection to facilitate new ways of encountering and experiencing art. We present the design and evaluation of an interactive application called SMKExplore, which allows users to explore a museum’s digital collection of paintings by browsing through objects detected in the images, as a novel form of open-ended exploration. We provide three contributions. First, we show how an object detection pipeline can be integrated into a design process for visual exploration. Second, we present the design and development of an app that enables exploration of an art museum’s collection. Third, we offer reflections on future possibilities for museums and HCI researchers to incorporate object detection techniques into the digitalization of museums.

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.

The current work investigates how creativity manifests when designers use data work in the early phase of design. Designers are increasingly interested in utilizing the massive amounts of data surrounding our everyday lives. However, data work is still challenging to incorporate into the design process. In this paper, we present a case study with three novice design teams who were tasked to integrate data work into their design process. During the study, we observed how creativity took place in framing a design problem. We present and discuss their actions from a creativity process perspective, highlighting how they used and rationalized data-inspired inquiries creatively in the early phase of design. The current results inform the development of a design framework to structure data work methodologically and coherently into design processes. We coin this design framework Exploratory Data Inquiry.