Perception-based lossy haptic compression considerations for velocity-based interactions

Zadeh, Mehrdad Hosseini; Wang, David; Kubica, Eric

doi:10.1007/s00530-007-0106-9

Cited by 67 publications

(24 citation statements)

References 12 publications

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“…In all these applications, unlike previous research, the motion of a user's hand and the virtual haptic object is important. The result in [16,11,31,30] shows a significant variation in human perception and the force thresholds while the velocity of the subject's hand is changed, albeit in a different scenario than is studied in this paper.…”

Section: Related Workmentioning

confidence: 98%

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Active Manipulation of Users in Haptic-Enabled Virtual Environments

Zadeh¹,

Wang²,

Kubica³

2008

Proceedings of the First International Conference on Ambient Media and Systems

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The main goal of this research is to study the effect of subthreshold forces on human performance in a haptic-enabled virtual reality system. A multi-modal task similar to Fitts is used to study the effects of the sub-threshold forces on user performance. Each user's movement is manipulated using controlled forces such that the user is not aware of the forces. Subjects can see the position of the haptic probe in a virtual environment where they are manipulated using sub-threshold forces. The multi-modal task is used to measure the accuracy of subjects in two experiments. During the experiments, the effects of force intensity and the relative direction of applied forces to the direction of user's hand motion in the presence of visual cues are investigated. A performance index is also introduced that can be used to evaluate human performance in the application of subthreshold forces. A psychophysical method is utilized to ensure that the applied forces on the user's hand are below the force threshold of the human haptic system. Results indicate that user performance is affected by both the intensity and direction of sub-threshold forces even when the users could control their actions through visual feedbacks.

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Section: Related Workmentioning

confidence: 98%

“…al [31] determined the absolute force thresholds of the human haptic system for application to a psychophysically motivated lossy haptic (force) compression technique. The compression technique store the haptic data only when the force exceeds a certain threshold of the human haptic perception.…”

Section: Related Workmentioning

confidence: 99%

Active Manipulation of Users in Haptic-Enabled Virtual Environments

Zadeh¹,

Wang²,

Kubica³

2008

Proceedings of the First International Conference on Ambient Media and Systems

Self Cite

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“…Since we are dealing with haptic force perception, the threshold value for ψ can be selected as 0.03 N following the absolute threshold of human perception [53]. The absolute threshold is the minimum amount of force that a human can perceive.…”

Section: Parameter Selectionmentioning

confidence: 99%

Stimuli-Magnitude-Adaptive Sample Selection for Data-Driven Haptic Modeling

Abdulali

Hassan

Jeon

2016

Entropy

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Data-driven haptic modeling is an emerging technique where contact dynamics are simulated and interpolated based on a generic input-output matching model identified by data sensed from interaction with target physical objects. In data-driven modeling, selecting representative samples from a large set of data in a way that they can efficiently and accurately describe the whole dataset has been a long standing problem. This paper presents a new algorithm for the sample selection where the variances of output are observed for selecting representative input-output samples in order to ensure the quality of output prediction. The main idea is that representative pairs of input-output are chosen so that the ratio of the standard deviation to the mean of the corresponding output group does not exceed an application-dependent threshold. This output-and standard deviation-based sample selection is very effective in applications where the variance or relative error of the output should be kept within a certain threshold. This threshold is used for partitioning the input space using Binary Space Partitioning-tree (BSP-tree) and k-means algorithms. We apply the new approach to data-driven haptic modeling scenario where the relative error of the output prediction result should be less than a perceptual threshold. For evaluation, the proposed algorithm is compared to two state-of-the-art sample selection algorithms for regression tasks. Four kinds of haptic related behavior-force datasets are tested. The results showed that the proposed algorithm outperformed the others in terms of output-approximation quality and computational complexity.

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“…Therefore, sophisticated packet rate reduction schemes are necessary to support haptic interactions over current Internet. Existing packet rate reduction schemes for haptic events can be classified into two groups: deadband-based [18,22,48] and predictionbased [11,32] filterings. The deadband-based filtering schemes transmit haptic events only if the change in events exceeds a given threshold value.…”

Section: Introductionmentioning

confidence: 99%

Priority-based haptic event filtering for transmission and error control in networked virtual environments

Lee

Kim

2009

Multimedia Systems

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A data filtering scheme is proposed for transmission and error control of haptic events in hapticbased network virtual environments; this scheme is called as priority-based haptic event filtering. Because a high update rate of approximately 1 kHz is required for haptic rendering, sophisticated transmission rate control and reduction schemes are necessary for the haptic events. Although existing schemes can reduce the transmission rate without any perception impairment, they are very sensitive to packet losses. In this paper, we prioritize the haptic events according to the delay and loss effects. Utilizing the proposed haptic event prioritization, the proposed filtering scheme adapts the transmission rate and updates the predicted loss rate according to the current network state. Our simulation and experiment results confirm that the proposed scheme can effectively select important haptic events and guarantee an improved haptic interaction quality over a bandwidth-limited lossy network than existing transmission schemes tailored for networked haptics.

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Perception-based lossy haptic compression considerations for velocity-based interactions

Cited by 67 publications

References 12 publications

Active Manipulation of Users in Haptic-Enabled Virtual Environments

Active Manipulation of Users in Haptic-Enabled Virtual Environments

Stimuli-Magnitude-Adaptive Sample Selection for Data-Driven Haptic Modeling

Priority-based haptic event filtering for transmission and error control in networked virtual environments

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