Extended multitouch

Murugappan, Sundar; Vinayak,; Elmqvist, Niklas; Ramani, Karthik

doi:10.1145/2380116.2380177

Cited by 50 publications

(9 citation statements)

References 33 publications

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“…A multitude of studies on touch detection have reported taking advantage of depth devices, such as the use of stereo cameras [10] or the combination of fixed depth and thermal cameras [11]. Wilson [12] utilized a single fixed depth camera to sense touch on a tabletop, the similar environment has been implemented in [13]. However, these background modeling approaches do not work sufficiently well in our daily environments because of the rapid background changes caused by head motions.…”

Section: Related Researchmentioning

confidence: 99%

Hotspot Modeling of Hand-Machine Interaction Experiences from a Head-Mounted RGB-D Camera

Chen

Nakamura

Kondo

et al. 2019

IEICE Trans. Inf. & Syst.

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This paper presents an approach to analyze and model tasks of machines being operated. The executions of the tasks were captured through egocentric vision. Each task was decomposed into a sequence of physical hand-machine interactions, which are described with touch-based hotspots and interaction patterns. Modeling the tasks was achieved by integrating the experiences of multiple experts and using a hidden Markov model (HMM). Here, we present the results of more than 70 recorded egocentric experiences of the operation of a sewing machine. Our methods show good potential for the detection of hand-machine interactions and modeling of machine operation tasks.

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Section: Related Researchmentioning

confidence: 99%

Hotspot Modeling of Hand-Machine Interaction Experiences from a Head-Mounted RGB-D Camera

Chen

Nakamura

Kondo

et al. 2019

IEICE Trans. Inf. & Syst.

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“…Benko et al [3] used EMG to sense users' hand movements and demonstrate a range of enhanced touch interactions, which included pressure detection, contact finger identification and off-surface pinch, throw and flick gestures. Murugappan et al [35] used a Kinect depth-camera to develop a range of enhanced touch interactions including the identification of the contact finger and the recovery of hand posture. Marquardt et al [32] presented a broad range of touch interaction techniques that extended the traditional vocabulary of interaction with an interactive tabletop, by tracking users' gestures above the surface with a Vicon motion tracking system.…”

Section: Related Workmentioning

confidence: 99%

“…A number of researchers have investigated different methods to infer touch pressure, and the interaction opportunities made possible by this [13,21,38,39,40]. While they have the potential to support a selection of rich and expressive interactions, they depend on specialized hardware, with [35,32] requiring a static camera-based tracking system that would not be appropriate for a mobile device context. Other methods have been proposed to provide enhanced touch interactions via pressure using the hardware available in a commodity smartphone.…”

Section: Related Workmentioning

confidence: 99%

“…While some of the technologies discussed are aimed at making performance of common discrete commands quicker or more natural [5,6,11,13,14,19,21,24,29], others demonstrate a potential to support more expressive interactions [22,35]. However, the majority of these approaches rely on bespoke or impractical hardware configurations [3,10,27,32,44] and many only extend the expressiveness of touch interaction with a single or small number of new capabilities [12,17,20,26,38,42,46,47].…”

Section: Related Workmentioning

confidence: 99%

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Expressy

Wilkinson

Kharrufa

Hook

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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Expressiveness, which we define as the extent to which rich and complex intent can be conveyed through action, is a vital aspect of many human interactions. For instance, paint on canvas is said to be an expressive medium, because it affords the artist the ability to convey multifaceted emotional intent through intricate manipulations of a brush. To date, touch devices have failed to offer users a level of expressiveness in their interactions that rivals that experienced by the painter and those completing other skilled physical tasks. We investigate how data about hand movementprovided by a motion sensor, similar to those found in many smart watches or fitness trackers -can be used to expand the expressiveness of touch interactions. We begin by introducing a conceptual model that formalizes a design space of possible expressive touch interactions. We then describe and evaluate Expressy, an approach that uses a wrist-worn inertial measurement unit to detect and classify qualities of touch interaction that extend beyond those offered by today's typical sensing hardware. We conclude by describing a number of sample applications, which demonstrate the enhanced expressive interaction capabilities made possible by Expressy.

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“…Other prototypes developed in the literature are mainly based on contacts geometry [1,5,9,31,18,29], RGB and depth cameras [4,11,13,17,24,30,33], and fingerprints [27,16].…”

mentioning

confidence: 99%

WhichFingers

Masson

Goguey

Malacria

et al. 2017

Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology

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HCI researchers lack low-latency and robust systems to support the design and development of interaction techniques using finger identification. We developed a low-cost prototype using piezo-based vibration sensors attached to each finger. By combining the events from an input device with the information from the vibration sensors we demonstrate how to achieve low-latency and robust finger identification. Our prototype was evaluated in a controlled experiment, using two keyboards and a touchpad, showing single-touch recognition rates of 98.2% for the keyboard and 99.7% for the touchpad, and 94.7% for two simultaneous touches. These results were confirmed in an additional laboratory-style experiment with ecologically valid tasks. Last we present new interaction techniques made possible using this technology.

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Extended multitouch

Cited by 50 publications

References 33 publications

Hotspot Modeling of Hand-Machine Interaction Experiences from a Head-Mounted RGB-D Camera

Hotspot Modeling of Hand-Machine Interaction Experiences from a Head-Mounted RGB-D Camera

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