SideSwipe

Zhao, Chen; Chen, Ke-Yu; Aumi, Tanvir Islam; Patel, Shwetak; Reynolds, Matthew S.

doi:10.1145/2642918.2647380

Cited by 54 publications

(3 citation statements)

References 22 publications

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“…Gesture detection based on Radio Frequency (RF) relies on analyzing the reflection of electromagnetic waves on the human body. Examples of detection systems specific to hand gestures include: WiSee [32], AllSee [18], SideSwipe [49], and WiTrack [1]. These systems typically require the user to remain within the range of a deployed infrastructure (e.g., WiFi router or specialized radar) in order to track a user's gestures.…”

Section: Radio Frequency Based Gesture Systemsmentioning

confidence: 99%

A Scalable Solution for Signaling Face Touches to Reduce the Spread of Surface-based Pathogens

Rojas

Poulsen

Tuyl

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Hand-to-Face transmission has been estimated to be a minority, yet non-negligible, vector of COVID-19 transmission and a major vector for multiple other pathogens. At the same time, as it cannot be effectively addressed with mainstream protection measures, such as wearing masks or tracing contacts, it remains largely untackled. To help address this issue, we have developed Saving Face - an app that alerts users when they are about to touch their faces, by analyzing the distortion patterns in the ultrasound signal emitted by their earphones. The system only relies on pre-existing hardware (a smartphone with generic earphones), which allows it to be rapidly scalable to billions of smartphone users worldwide. This paper describes the design, implementation and evaluation of the system, as well as the results of a user study testing the solution's accuracy, robustness, and user experience during various day-to-day activities (93.7% Sensitivity and 91.5% Precision, N=10). While this paper focuses on the system's application to detecting hand-to-face gestures, the technique can also be applicable to other types of gestures and gesture-based applications.

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Section: Radio Frequency Based Gesture Systemsmentioning

confidence: 99%

A Scalable Solution for Signaling Face Touches to Reduce the Spread of Surface-based Pathogens

Rojas

Poulsen

Tuyl

et al. 2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…Despite popular technologies used for implementing gesture recognisers such as RGB [27,52,58] or infrared (IR) [11,15,21,53,54] cameras, RF-based solutions including radar [29,40], Wi-Fi [2,41,69], GSM [68], and RFID [14] offer several advantages. Above all, RF sensing technologies are insensitive to light, which usually affects camera and, especially, IR based solutions (both cannot be used in bright sunlight).…”

Section: Rf Sensing Technologiesmentioning

confidence: 99%

Solids on Soli

Pucihar

Attygalle

Kljun

et al. 2022

Proc. ACM Hum.-Comput. Interact.

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Gesture recognition with miniaturised radar sensors has received increasing attention as a novel interaction medium. The practical use of radar technology, however, often requires sensing through materials. Yet, it is still not well understood how the internal structure of materials impacts recognition performance. To tackle this challenge, we collected a large dataset of 14,090 radar recordings for 6 paradigmatic gesture classes sensed through a variety of everyday materials, performed by humans (6 materials) and a robot system (75 materials). Next, we developed a hybrid CNN+LSTM deep learning model and derived a robust indirect method to measure signal distortions, which we used to compile a comprehensive catalogue of materials for radar-based interaction. Among other findings, our experiments show that it is possible to estimate how different materials would affect gesture recognition performance of arbitrary classifiers by selecting just 3 reference materials. Our catalogue, software, models, data collection platform, and labeled datasets are publicly available.

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“…A trend resulting from advances in sensing technologies is expanding the interaction space of touch screen devices, such as mobiles and tablets, above and around the actual touchsensitive screens (e.g., [25,31,39,41]). This trend shows promise to address many of the limitations in unimodal touch interaction with interactive surfaces by supporting alternative input techniques and combining them with touch input.…”

Section: Multimodal Inputmentioning

confidence: 99%

Exploring the User Experience of Proxemic Hand and Pen Input Above and Aside a Drawing Screen

Aslan

Bittner

Müller

et al. 2018

Proceedings of the 17th International Conference on Mobile and Ubiquitous Multimedia

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Digital drawing experiences are not only fused by the flexibility of digital materials but also influenced by the availability of interaction space. In this paper, we first present a prototype, which implements a method to turn the (mid-air) space above and aside a drawing screen in a desktop setting dynamically into sensory space for gestural and spatial input. Then we report on a user study exploring how participants experience digital drawing when the additional interaction space above and aside a screen is exploited for exemplary proxemic input techniques for zooming and panning a drawing. Our results show that the new multimodal input techniques are perceived as significantly more attractive than a baseline drawing condition which only utilizes touch based input. We conclude by discussing implications and limitations of our findings and input above and aside a drawing screen in general. CCS Concepts •Human-centered computing → Interaction techniques;

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SideSwipe

Cited by 54 publications

References 22 publications

A Scalable Solution for Signaling Face Touches to Reduce the Spread of Surface-based Pathogens

A Scalable Solution for Signaling Face Touches to Reduce the Spread of Surface-based Pathogens

Solids on Soli

Exploring the User Experience of Proxemic Hand and Pen Input Above and Aside a Drawing Screen

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