MatchPoint

Clarke, Christopher; Gellersen, Hans

doi:10.1145/3126594.3126626

Cited by 17 publications

(2 citation statements)

References 62 publications

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“…However, as yet there is no established success rate prediction model based on the path length and width. Manual pursuit of a moving target is required in hand-gesturing UIs [13], video games, and camera controls. For example, in firstperson shooter games, a player may need to continuously shoot a machine gun at a moving tank, or in rhythm games, a player slides a finger inside a target window that moves horizontally at a constant pace (e.g., Arcaea).…”

Section: Introductionmentioning

confidence: 99%

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Yamanaka¹,

Usuba

Takahashi

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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We propose a Servo-Gaussian model to predict success rates in continuous manual tracking tasks. Two tasks were conducted to validate this model: path steering and pursuit of a 1D moving target. We hypothesized that (1) hand movements follow the servo-mechanism model, (2) submovement endpoints form a bivariate Gaussian distribution, thus enabling us to predict the success rate at which a submovement endpoint falls inside the tolerance, and (3) the success rate for a whole trial can be predicted if the number of submovements is known. The cross-validation showed R 2 > 0.92 and MAE < 4.9% for steering and R 2 > 0.95 and MAE < 6.5% for pursuit tasks. These results demonstrate that our proposed model delivers high prediction accuracy even for unknown datasets.

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Section: Introductionmentioning

confidence: 99%

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Yamanaka¹,

Usuba

Takahashi

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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“…Related work has developed and studied a variety of technical implementations of motion matching interaction, using webcams [11,12], depth-sensors [9], eye-trackers [18,25,37,48,52], magnets [39], and inertial measurement units (IMUs) embedded in smart-watches [50], phones [4], and AR headsets [19]. These implementations are supplemented with work on further algorithmic developments and novel deployments [10,16,21,27,29,47].…”

Section: Introductionmentioning

confidence: 99%

Designing Motion Matching for Real-World Applications

Verweij

Esteves

Bakker

et al. 2019

Proceedings of the Thirteenth International Conference on Tangible, Embedded, and Embodied Interaction

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Amongst the variety of (multi-modal) interaction techniques that are being developed and explored, the Motion Matching paradigm provides a novel approach to selection and control. In motion matching, users interact by rhythmically moving their bodies to track the continuous movements of different interface targets. This paper builds upon the current algorithmic and usability focused body of work by exploring the product possibilities and implications of motion matching. Through the development and qualitative study of four novel and different real-world motion matching applications-with 20 participants-we elaborate on the suitability of motion matching in different multiuser scenarios, the less pertinent use in home environments and the necessity for multi-modal interaction. Based on these learnings, we developed three novel motion matching based interactive lamps, which report on clear paths for further dissemination of the embodied interaction technique's experience. This paper hereby informs the design of future motion matching interfaces and products. CCS Concepts •Human-centered computing → Interaction techniques; Interactive systems and tools;

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Wattom: Ambient Eco-feedback with Mid-air Input

Esteves

Quintal

Caires

et al. 2019

2019 5th Experiment International Conference (exp.at'19)

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This paper presents Wattom, a highly interactive ambient eco-feedback smart plug that aims to promote a more sustainable use of electricity in the home. This paper describes our latest implementation of the Wattom plug, and three system applications. The first enables Wattom to power a connected device, and provide real-time feedback on the amount of electricity in the grid from renewable sources. The second enables users to schedule power events from their smart watches. Finally, the third application uses non-intrusive load monitoring (NILM) to provide users with personal consumption information on multiple devices connected to a single Wattom plug. The paper concludes by presenting insights into the development and use of various iterations of the Wattom plug.

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MatchPoint

Cited by 17 publications

References 62 publications

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Designing Motion Matching for Real-World Applications

Wattom: Ambient Eco-feedback with Mid-air Input

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