research-article

Seeing the Wind: An Interactive Mist Interface for Airflow Input

Authors:

Tian Min,

Chengshuo Xia,

Takumi Yamamoto,

Yuta SugiuraAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 7, Issue ISS

Article No.: 444, Pages 398 - 419

https://doi.org/10.1145/3626480

Published: 01 November 2023 Publication History

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Abstract

Human activities can introduce variations in various environmental cues, such as light and sound, which can serve as inputs for interfaces. However, one often overlooked aspect is the airflow variation caused by these activities, which presents challenges in detection and utilization due to its intangible nature. In this paper, we have unveiled an approach using mist to capture invisible airflow variations, rendering them detectable by Time-of-Flight (ToF) sensors. We investigate the capability of this sensing technique under different types of mist or smoke, as well as the impact of airflow speed. To illustrate the feasibility of this concept, we created a prototype using a humidifier and demonstrated its capability to recognize motions. On this basis, we introduce potential applications, discuss inherent limitations, and provide design lessons grounded in mist-based airflow sensing.

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Video (iss23main-p4798-p-video.mp4)

This is a presentation video for paper, Seeing the Wind: An Interactive Mist Interface for Airflow Input. In this paper, we investigate the capability of mist sensing technique under different types of mist or smoke, as well as the impact of airflow speed. To illustrate the feasibility of this concept, we created a prototype using a humidifier and demonstrated its capability to recognize motions. On this basis, we introduce potential applications, discuss inherent limitations, and provide design lessons grounded in mist-based airflow sensing.

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185.51 MB

Teaser (iss23main-p4798-p-teaser.mp4)

This is the teaser video for ISS 2023 paper of Seeing the Wind: An Interactive Mist Interface for Airflow Input. In this paper, we have unveiled an approach using mist to capture invisible airflow variations, rendering them detectable by Time-of-Flight (ToF) sensors. We investigate the capability of this sensing technique under different types of mist or smoke, as well as the impact of airflow speed. To illustrate the feasibility of this concept, we created a prototype using a humidifier and demonstrated its capability to recognize motions. On this basis, we introduce potential applications, discuss inherent limitations, and provide design lessons grounded in mist-based airflow sensing.

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237.62 MB

References

[1]

Karan Ahuja, Yue Jiang, Mayank Goel, and Chris Harrison. 2021. Vid2Doppler: Synthesizing Doppler radar data from videos for training privacy-preserving activity recognition. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA. 1–10. https://doi.org/10.1145/3411764.3445138

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