A quantitative evaluation of haptic data prediction techniques over best-effort network

Tee, Tiam Hee; Yap, Kian Meng; Marshall, Alan; Eu, Kok Seng

doi:10.1109/icact.2014.6779065

Cited by 2 publications

(2 citation statements)

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“…Therefore, the proposed framework presented in this paper aims to further enhance the real-time synchronization of the tele-haptic interaction in the presence of network impairments, such as jitters and packet losses, while maintaining the accuracy and consistency of the smooth haptic updates. We have adopted the quantitative evaluation technique of the haptic data prediction proposed in our previous work [51]. The proposed TSP framework can estimate the position data of the haptic data using the velocity and yank estimations.…”

Section: Haptic Data Processing Using Predictive Approaches Under mentioning

confidence: 99%

“…A new network-adaptive Trust Strategy Prediction (TSP) is proposed to compensate for network impairments, by predicting the real-time haptic data. The TSP framework is adopted from our previous work [51], which predicted the velocity and yank estimations using positional data, based on historical haptic data and connectivity information. The results have been shown to produce better accuracy and less inconsistency, compared to the two well-known and popular positional prediction techniques used in networked haptics, which are the Dead Reckoning (DR) position estimation, and the Velocity Estimation with filtering (VE+F) technique [6] [7].…”

Section: Introductionmentioning

confidence: 99%

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A Network-Adaptive Prediction Algorithm for Haptic Data Under Network Impairments

et al. 2021

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Real-time tele-haptic applications require capturing, compressing, transmitting, and displaying haptic information, which includes tactile and kinesthetic information. To achieve a high quality of service (QoS), real-time haptic data stream synchronization between local and remote environments is required. However, transmission of data over a computer network is often affected by network impairments, such as network delay, jitter, and packet loss, thus leading to system instability and poor performance. Current prediction algorithms for networked haptics comprise perceptual data reduction, traffic prioritization approaches, congestion control approaches, and radio resource allocation. However, the mentioned prediction algorithms either do not consider packet loss and time-varying delays (i.e., jitter) in their experimental setup, or only consider packet loss or delays. In real-world network environments, both packet loss and delays often occur simultaneously. In this work, a network adaptive Trust Strategy Prediction (TSP) algorithm was modified to work under both network impairments. The objective of the TSP is to maintain real-time haptic synchronization (haptic data stream synchronization) between the haptic interactive environments, by compensating network impairments using selective and specific prediction strategies, according to changes in the network's characteristics. The experimental results demonstrate that TSP offers greater accuracy and smaller inconsistencies in terms of the predicted position, compared to the dead reckoning prediction and velocity estimation, which is often employed with filtering techniques.

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Section: Haptic Data Processing Using Predictive Approaches Under mentioning

confidence: 99%