TapSkin

Zhang, Cheng; Bedri, Abdelkareem; Reyes, Germán Morong; Bercik, Bailey; Inan, Omer T.; Starner﻿﻿, Thad; Abowd, Gregory D.

doi:10.1145/2992154.2992187

Cited by 72 publications

(5 citation statements)

References 30 publications

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“…Work done on wrist watches has extended gestures from just the dial to the wristband [63]. Zhang et al introduce gestures to bezels, sides and the band of the watch in WatchOut [89] and use the skin around the wrist as input in TapSkin [87]. Beats [60] explores the design space of 'beating gestures' which are repeated sequential taps made by the index and middle finger of one hand.…”

Section: On-body and On-device Wearable Gesturesmentioning

confidence: 99%

Exploring the Design Space of Badge Based Input

Bhatia

Ahuja

Agarwal

et al. 2020

Proc. ACM Hum.-Comput. Interact.

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Human Body & Clothing Badge Badge Body Fastener Fig. 1. Badge-based interaction gestures are enabled by the body of the badge and the method of attaching the badge to the clothing. In this paper, we explore input with wearables that can be attached and detached at will from any of our regular clothes. These wearables do not cause any permanent effect on our clothing and are suitable to be worn anywhere, thus making them very similar to badges we wear. To explore this idea of non-permanent badge input, we studied various methods to fasten objects to our clothing and organise them in the form of a design space. We leverage this synthesis, along with literature and existing products to present possible interaction gestures these badge-based wearables can enable.

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Section: On-body and On-device Wearable Gesturesmentioning

confidence: 99%

Exploring the Design Space of Badge Based Input

Bhatia

Ahuja

Agarwal

et al. 2020

Proc. ACM Hum.-Comput. Interact.

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“…Various sensing approaches have been presented for detecting hand gestures. Camera-based approaches [4,8,14,20,27,29,43,51], electromyography-based approaches [37,38] and bioacoustic approaches [1,10,14,21,52] have demonstrated the recognition of one-hand gestures. Passive techniques are also proposed [22].…”

Section: Sensing Hand Gesturesmentioning

confidence: 99%

Grasping Microgestures

Sharma

Roo

Steimle

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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“…For example, Amento et al [1], Hambone [8], Skinput [15], and Tactile Teacher [16] place passive acoustic sensors on the skin to listen to micro-vibrations resulted from finger taps, flicks, and pinches. More recently, research has shown that off-the-shelf smartwatches can also detect these signals [23] [32][49] [52]. Way et.…”

Section: Hand Gesture Sensingmentioning

confidence: 99%

BeamBand

Iravantchi

Goel

Harrison

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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BeamBand is a wrist-worn system that uses ultrasonic beamforming for hand gesture sensing. Using an array of small transducers, arranged on the wrist, we can ensemble acoustic wavefronts to project acoustic energy at specified angles and focal lengths. This allows us to interrogate the surface geometry of the hand with inaudible sound in a raster-scan-like manner, from multiple viewpoints. We use the resulting, characteristic reflections to recognize hand pose at 8 FPS. In our user study, we found that BeamBand supports a six-class hand gesture set at 94.6% accuracy. Even across sessions, when the sensor is removed and reworn later, accuracy remains high: 89.4%. We describe our software and hardware, and future avenues for integration into devices such as smartwatches and VR controllers. CCS CONCEPTS Human-centered computing → Human computer interaction (HCI) → Interaction techniques → Gestural input

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TapSkin

Cited by 72 publications

References 30 publications

Exploring the Design Space of Badge Based Input

Exploring the Design Space of Badge Based Input

Grasping Microgestures

BeamBand

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