The CHIIoT workshop series brings together researchers and practitioners from human-computer interaction (HCI) design, computer science, and electrical engineering working on new challenges in industry and academia. In EICS 2021, This workshop will provide a platform for participants to review and discuss challenges and opportunities in the intersection of computer-human interaction and the internet of things, focusing on human-centered applications using emerging connectivity and sensing technologies. We aim to jointly develop a design space and identify opportunities for future research.

The CHIIoT workshops bring together researchers and practitioners from industrial design, computer science, and electrical engineering working on new challenges in industry and academia. The workshop will provide a platform for participants to review and discuss challenges and opportunities in the intersection of computer-human interaction and the internet of things, focusing on human-centered applications using emerging connectivity and sensing technologies. We aim to jointly develop a design space and identify opportunities for future research.

While sleep health is increasingly becoming important to many people, it is challenging to maintain good sleep quality. Because sleep can be affected by many internal and external factors, which may differ among individuals, it is therefore important to support people to explore and reflect on what factors truly affect their sleep as most existing sleep self-tracking tools mainly focus on showing how individuals slept. In this study, we developed two design probes to support users to investigate personal sleep factors through self-experimenting and reflection. The tangible probe enables individuals to track three personal sleep factors flexibly, while the digital visualization one provides weekly reports of their sleep together with the three tracked factors. We conducted a preliminary field study with five participants over four weeks. The results show that our designs could support users to investigate their personal concerned sleep factors in terms of the causal relationship with the subjective sleep quality.

As sleep health is increasingly becoming important in recent years, many wearable products and mobile apps have been developed to track users' sleep and interpret their sleep quality. Most of the available designs have mainly focused on objective measurements, such as body movement, heart rate, and/or bedroom light, noise level, and temperature. However, due to the lack of users' subjective experience measurements, sleep trackers often fail to provide useful suggestions for improving their sleep. In this study, we developed SlumberBot with conversational chatbot technology to help users capture their subjective sleep experiences and relevant factors in daytime activities as well. With SlumberBot, we conducted a preliminary field study with five participants in a 4-week time period. The result shows that SlumberBot is easy to stay engaged with and supportive of users' self-reflection on contextual factors related to sleep quality. Besides, SlumberBot has shown the potential of triggering short-term behavior changes that would impact their sleep positively.