Motion and Force Prediction in Haptic Media

Sakr, Nizar; Georganas, N.D.; Zhao, Jiying; Shen, Xiaojun

doi:10.1109/icme.2007.4285132

Cited by 27 publications

(14 citation statements)

References 4 publications

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“…For instance, [82] employed a prediction method for three-dimensional position and force data by means of an advanced first-order autoregressive (AR) model. After an initialization and training process, the adaptive coefficients of the model are computed for the predicted values to be produced.…”

Section: B Tactile Artificial Intelligence Enginesmentioning

confidence: 99%

5G-Enabled Tactile Internet

Simsek

Aijaz

Dohler

et al. 2016

IEEE J. Select. Areas Commun.

707

430

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Abstract-The long-term ambition of the Tactile Internet is to enable a democratization of skill, and how it is being delivered globally. An integral part of this is to be able to transmit touch in perceived real-time, which is enabled by suitable robotics and haptics equipment at the edges, along with an unprecedented communications network. The fifth generation (5G) mobile communications systems will underpin this emerging Internet at the wireless edge. This paper presents the most important technology concepts which lay at the intersection of the larger Tactile Internet and the emerging 5G systems. The paper outlines the key technical requirements and architectural approaches for the Tactile Internet, pertaining to wireless access protocols, radio resource management aspects, next generation core networking capabilities, edge-cloud and edge-AI capabilities. The paper also highlights the economic impact of the Tactile Internet as well as a major shift in business models for the traditional telecommunications ecosystem.

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Section: B Tactile Artificial Intelligence Enginesmentioning

confidence: 99%

5G-Enabled Tactile Internet

Simsek

Aijaz

Dohler

et al. 2016

IEEE J. Select. Areas Commun.

707

430

View full text Add to dashboard Cite

show abstract

“…Predicting targets in a GUI is not a new concept, and a number of researchers have applied different approaches to this problem [5,6,17,21]. The desire to predict the target in this instance is to overcome interference that causes distractions created when overlapping haptic effects interact in a multiple-target environment.…”

Section: Target Prediction Methodsmentioning

confidence: 99%

“…Sakr et al [21] have developed a prediction algorithm that is successful at approximating the next movement and haptic effect in an immersive environment. The approach relies on an auto-regressive model in which at least three previous movements and haptic effects are used to determine the next movement and haptic effect.…”

Section: Target Prediction Methodsmentioning

confidence: 99%

Design and evaluation of haptic effects for use in a computer desktop for the physically disabled

Holbert

Huber

2010

Univ Access Inf Soc

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The human-computer interface remains a mostly visual environment with little or no haptic interaction. While haptics is finding inroads in specialized areas such as surgery, gaming, and robotics, there has been little work to bring haptics to the computer desktop, which is largely dominated today by the GUI/mouse relationship. The mouse as an input device, however, poses many challenges for users with physical disabilities, and it is believed that a haptically enhanced interface could have significant impact assisting in target selection. This paper presents a study intended to evaluate haptic effects used with a force feedback mouse on a computer desktop and a prediction algorithm designed to focus those effects on the desired target. Results of the experiment were partially successful and indicated future directions for improvement. The paper introduces the proposed framework and presents experimental results from targeting tasks using differing haptic effects with a group of physically disabled users.

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“…The key idea is to periodically readjust the sampling rate according to a current measured bandwidth of the haptic signals and therefore reduces the necessary storage requirements. A method to predict motion and force information in haptic media is proposed in [10]. In [11], an architecture for compressing haptic media files is presented which is related to this work.…”

Section: Related Workmentioning

confidence: 99%

Deadband-based offline-coding of haptic media

Kammerl

Steinbach

2008

Proceedings of the 16th ACM International Conference on Multimedia

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In this work, a novel perceptual coding approach for offline compression of haptic media is presented. Our scheme exploits the properties of human haptic perception and hides coding artefacts introduced by lossy compression below the human perception thresholds. We combine the concept of Just Noticeable Differences with predictive coding in order to achieve high coding efficiency without impairing user perception. In our experiments, we apply the proposed lossy compression scheme to haptic data that has been recorded during a telemanipulation session in a virtual environment. Our user studies reveal that our approach leads to strong data reduction up to 100:1 while preserving a high-quality haptic experience during playback of the compressed haptic data streams.

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Motion and Force Prediction in Haptic Media

Cited by 27 publications

References 4 publications

5G-Enabled Tactile Internet

5G-Enabled Tactile Internet

Design and evaluation of haptic effects for use in a computer desktop for the physically disabled

Deadband-based offline-coding of haptic media

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