Telepresence across delayed networks: a combined prediction and compression approach

Clarke, Stella; Schillhuber, G.; Zaeh, Michael F.; Ulbrich, H.

doi:10.1109/have.2006.283795

Cited by 23 publications

(12 citation statements)

References 10 publications

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“…However, all these approaches introduce an additional delay due to the processing of blocks of signal and are not ideal for applications such as telesurgery. Another strand of works has focused on the exploitation of Weber's law for haptic signal digitization [Clarke et al 2006;Dabeer and Chaudhuri 2011;Hinterseer et al 2005Hinterseer et al , 2008Hirche et al 2007;Sakr et al 2009b;Steinbach et al 2011;Vittorias et al 2009;Lee and Payandeh 2011]. According to Weber's law, the relative change in the signal determines its perception.…”

Section: Related Workmentioning

confidence: 99%

Design and Analysis of Predictive Sampling of Haptic Signals

Bhardwaj

Chaudhuri

Dabeer

2014

ACM Trans. Appl. Percept.

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In this article, we identify adaptive sampling strategies for haptic signals. Our approach relies on experiments wherein we record the response of several users to haptic stimuli. We then learn different classifiers to predict the user response based on a variety of causal signal features. The classifiers that have good prediction accuracy serve as candidates to be used in adaptive sampling. We compare the resultant adaptive samplers based on their rate-distortion tradeoff using synthetic as well as natural data. For our experiments, we use a haptic device with a maximum force level of 3 N and 10 users. Each user is subjected to several piecewise constant haptic signals and is required to click a button whenever he perceives a change in the signal. For classification, we not only use classifiers based on level crossings and Weber's law but also random forests using a variety of causal signal features. The random forest typically yields the best prediction accuracy and a study of the importance of variables suggests that the level crossings and Weber's classifier features are most dominant. The classifiers based on level crossings and Weber's law have good accuracy (more than 90%) and are only marginally inferior to random forests. The level crossings classifier consistently outperforms the one based on Weber's law even though the gap is small. Given their simple parametric form, the level crossings and Weber's law-based classifiers are good candidates to be used for adaptive sampling. We study their rate-distortion performance and find that the level crossing sampler is superior. For example, for haptic signals obtained while exploring various rendered objects, for an average sampling rate of 10 samples per second, the level crossings adaptive sampler has a mean square error about 3dB less than the Weber sampler. ACM Reference Format:Amit Bhardwaj, Subhasis Chaudhuri, and Onkar Dabeer. 2014. Design and analysis of predictive sampling of haptic signals.

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Section: Related Workmentioning

confidence: 99%

Design and Analysis of Predictive Sampling of Haptic Signals

Bhardwaj

Chaudhuri

Dabeer

2014

ACM Trans. Appl. Percept.

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“…In [11,12], Clarke et al introduce a prediction-based technique to reduce the number of transmitted data packets and to compensate for network delay in haptic telepresence applications. Prediction is performed using double exponential smoothing, which essentially models a time series using a basic linear regression equation.…”

Section: Related Workmentioning

confidence: 99%

Prediction-based Haptic Data Reduction and Compression in Tele-Mentoring Systems

Sakr

Zhou

Georganas

et al. 2008

2008 IEEE Instrumentation and Measurement Technology Conference

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In this paper, a novel haptic data reduction and compression technique to reduce haptic data traffic in networked haptic tele-mentoring systems is presented. The suggested methodfollows a two-step procedure: (1) haptic data packets are not transmitted when they can be predicted within a predefined tolerable error; otherwise,(2) data packets are compressed prior to transmission. The prediction technique relies on the least-squares method. Knowledge from human haptic perception is incorporated into the architecture to assess the perceptual quality of the prediction results. Packet-payload compression is performed using uniform quantization and adaptive Golomb-Rice codes. The preliminary experimental results demonstrate the algorithm's effectiveness as great haptic data reduction and compression is achieved, while preserving the overall quality of the tele-mentoring environment.

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“…It essentially consists of a linear predictor that estimates the haptic signal based on the slope computed from the two last available signal values. In [3], a prediction and compression architecture is proposed in order to overcome network delays in telepresence applications. Both movement and force prediction models are considered in their architecture.…”

Section: Introductionmentioning

confidence: 99%

Motion and Force Prediction in Haptic Media

Sakr

Georganas

Zhao

et al. 2007

Multimedia and Expo, 2007 IEEE International Conference On

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This paper introduces a novel generic method aimed at predicting motion and force information in haptic media. An autoregressive (AR) model is presented for the prediction of both, haptic movement and force. The conditional maximum likelihood technique is utilized in order to accurately estimate the adaptive coefficients of the AR model. Furthermore, the incorporation of concepts from haptic perceptibility, i.e. the Just Noticeable Difference (JND), has been demonstrated to optimize the suggested algorithm, while preserving the immersiveness of the haptic-enabled environment. The proposed technique has also proved to provide accurate prediction results for non-linear haptic movement and force information while simultaneously remaining computationally efficient.

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Telepresence across delayed networks: a combined prediction and compression approach

Cited by 23 publications

References 10 publications

Design and Analysis of Predictive Sampling of Haptic Signals

Design and Analysis of Predictive Sampling of Haptic Signals

Prediction-based Haptic Data Reduction and Compression in Tele-Mentoring Systems

Motion and Force Prediction in Haptic Media

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