Motion Correlation

Velloso, Eduardo; Carter, Marcus; Newn, Joshua; Esteves, Augusto; Clarke, Christopher; Gellersen, Hans

doi:10.1145/3064937

Cited by 60 publications

(30 citation statements)

References 64 publications

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“…is characteristic is also shared by PathSync [4], a gesture technique where users mimic the motion of on-screen cursors using their hands ("motion correlation" [17]). Rhythmic micro-gestures share this property: the movement of the hand and ngers is important, not their position.…”

Section: Related Workmentioning

confidence: 99%

Rhythmic micro-gestures: discreet interaction on-the-go

Freeman

Griffiths

Brewster

2017

Proceedings of the 19th ACM International Conference on Multimodal Interaction

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We present rhythmic micro-gestures, micro-movements of the hand that are repeated in time with a rhythm. We present a user study that investigated how well users can perform rhythmic microgestures and if they can use them eyes-free with non-visual feedback. We found that users could successfully use our interaction technique (97% success rate across all gestures) with short interaction times, rating them as low di culty as well. Simple audio cues that only convey the rhythm outperformed animations showing the hand movements, supporting rhythmic micro-gestures as an eyes-free input technique.

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Section: Related Workmentioning

confidence: 99%

Rhythmic micro-gestures: discreet interaction on-the-go

Freeman

Griffiths

Brewster

2017

Proceedings of the 19th ACM International Conference on Multimodal Interaction

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“…Different implementations exist to detect Pursuits. For an overview on general pursuit detection methods we refer to Velloso et al [29] who discussed two detection methods: Euclidean distance and correlation. Herlina et al [13] report on a comparison of both methods.…”

Section: Implementations Of Pursuitsmentioning

confidence: 99%

“…For this reason, it works reliably without calibration [21,29,32,33] and even on small interfaces such that of smart watches [11]. On the downside, the accuracy of the correlationbased detection drops significantly in the presence of more than 8 targets [11,29,32]. A recent suggestion for improving circular smooth pursuit detection was proposed by Velloso et al [30].…”

Section: Implementations Of Pursuitsmentioning

confidence: 99%

DialPlates

Drewes

Khamis

Alt

2019

Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia

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In this paper we introduce a novel approach for smooth pursuits eye movement detection and demonstrate that it allows up to 160 targets to be distinguished. With this work we advance the wellestablished smooth pursuits technique, which allows gaze interaction without calibration. The approach is valuable for researchers and practitioners, since it enables novel user interfaces and applications to be created that employ a large number of targets, for example, a pursuits-based keyboard or a smart home where many different objects can be controlled using gaze. We present findings from two studies. In particular, we compare our novel detection algorithm based on linear regression with the correlation method. We quantify its accuracy for around 20 targets on a single circle and up to 160 targets on multiple circles. Finally, we implemented a pursuits-based keyboard app with 108 targets as proof-of-concept. CCS CONCEPTS • Human-centered computing → Interaction techniques.

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“…For a system to be able to differentiate which option is being selected, it needs to have a way of matching the user's input against the available targets. In conventional user interfaces, this match is either spatial (the user points at the position of a target) or semantic (the input is linked to a target by its meaning, for example when we press a button on a remote control, or type in a command) [9,32]. The approach in TraceMatch is fundamentally different: the user's input is matched against available targets based on motion.…”

Section: Background and Related Workmentioning

confidence: 99%

“…TraceMatch leverages insight from prior work on motion correlation as an input mechanism [32]. Fekete et al…”

mentioning

confidence: 99%

Remote Control by Body Movement in Synchrony with Orbiting Widgets

Clarke

Bellino

Esteves

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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In this work we consider how users can use body movement for remote control with minimal effort and maximum flexibility. TraceMatch is a novel technique where the interface displays available controls as circular widgets with orbiting targets, and where users can trigger a control by mimicking the displayed motion. The technique uses computer vision to detect circular motion as a uniform type of input, but is highly appropriable as users can produce matching motion with any part of their body. We present three studies that investigate input performance with different parts of the body, user preferences, and spontaneous choice of movements for input in realistic application scenarios. The results show that users can provide effective input with their head, hands and while holding objects, that multiple controls can be effectively distinguished by the difference in presented phase and direction of movement, and that users choose and switch modes of input seamlessly. CCS Concepts: • Human-centered computing → Interaction devices; Gestural input; Ubiquitous and mobile computing;

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Motion Correlation

Cited by 60 publications

References 64 publications

Rhythmic micro-gestures: discreet interaction on-the-go

Rhythmic micro-gestures: discreet interaction on-the-go

DialPlates

Remote Control by Body Movement in Synchrony with Orbiting Widgets

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