Kai Kunze

HCI Deep Dives

Science EN ↓ 57 episodes

HCI Deep Dives is your go-to podcast for exploring the latest trends, research, and innovations in Human Computer Interaction (HCI). Auto-generated using the latest publications in the field, each episode dives into in-depth discussions on topics like wearable computing, augmented perception, cognitive augmentation, and digitalized emotions. Whether you’re a researcher, practitioner, or just curious about the intersection of technology and human senses, this podcast offers thought-provoking insights and ideas to keep you at the forefront of HCI.

Author

Kai Kunze

Category

Science

Podcast website

www.deep-hci.org

Latest episode

Mar 6, 2026

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Episodes

Emolleia – Wearable Kinetic Flower Display for Expressing Emotions 05.10.2024

What we wear (our clothes and wearable accessories) can represent our mood at the moment. We developed Emolleia to explore how to make aesthetic wears more expressive to become a novel form of non-verbal communication to express our emotional feelings. Emolleia is an open wearable kinetic display in form of three 3D printed flowers that can dynamically open and close at different speeds. With our...

“I am both here and there” Parallel Control of Multiple Robotic Avatars by Disabled Workers in a Café 04.10.2024

Robotic avatars can help disabled people extend their reach in interacting with the world. Technological advances make it possible for individuals to embody multiple avatars simultaneously. However, existing studies have been limited to laboratory conditions and did not involve disabled participants. In this paper, we present a real-world implementation of a parallel control system allowing disabl...

Movement Quality Visualization for Wheelchair Dance 04.10.2024

Wheelchair dance is an important form of disability art that is still subject to significant levels of ableism and art exclusion. Wheelchair dancers face challenges finding teachers and choreographers who can accommodate their needs, documenting and sharing choreographies that suit their body shapes and their assistive technologies. In turn, this hinders their ability to share creative expressions...

Innermost Echoes: Integrating Real-Time Physiology into Live Music Performances 04.10.2024

In this paper, we propose a method for utilizing musical artifacts and physiological data as a means for creating a new form of live music experience that is rooted in the physiology of the performers and audience members. By utilizing physiological data (namely Electrodermal Activity (EDA) and Heart Rate Variability (HRV)) and applying this data to musical artifacts including a robotic koto (a tr...

"Speech is Silver, Silence is Golden " Analyzing Micro-communication Strategies between Visually Impaired Runners and their Guides 04.10.2024

Running and jogging are popular activities for many visually impaired individuals thanks to the relatively low entry barriers. Research in HCI and beyond has focused primarily on leveraging technology to enable visually impaired people to run independently. However, depending on their residual vision and personal preferences, many chose to run with a sighted guide. This study presents a comprehens...

Detecting an Offset-Adjusted Similarity Score based on Duchenne Smiles 03.10.2024

Detecting interpersonal synchrony in the wild through ubiquitous wearable sensing invites promising new social insights as well as the possibility of new interactions between humans-humans and humans-agents. We present the Offset-Adjusted SImilarity Score (OASIS), a real-time method of detecting similarity which we show working on visual detection of Duchenne smile between a pair of users. We cond...

Linking Audience Physiology to Choreography 03.10.2024

The use of wearable sensor technology opens up exciting avenues for both art and HCI research. To be effective, such work requires close collaboration between performers and researchers. In this article, we report on the co-design process and research insights from our work integrating physiological sensing and live performance.  https://dl.acm.org/doi/10.1145/3557887

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