A Long-Short-Term Memory-Based Model for Kinesthetic Data Reduction

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Abstract

This article proposes a novel mathematical model for teleoperation over communication networks. For teleoperation over a communication network, a high packet rate can result in inefficient data transmission and cross-traffic problems, leading to extra delay and jitter. This article proposes an long short-term memory (LSTM)-based mathematical model which focuses on kinesthetic data reduction without loss of transparency during the transmission process through joint training combined with haptic data and perceptual deadband. Since the LSTM network can deal with a time series of haptic data, we further test the system performance through practically collected data. We investigate the packet rate and perceptual transparency of the proposed mathematical model by comparing with the conventional deadband. Additionally, we compare the proposed mathematical model with the perceptual deadband-based codecs. Simulation results show that the proposed solution further reduces the packet rate when dealing with haptic data without noticeable distortion. Also, comparing with the current just noticeable difference perceptual threshold, the proposed mathematical model helps improve the practicality of the bilateral teleoperation system without losing transparency.

Original languageEnglish
Pages (from-to)16975-16988
Number of pages14
JournalIEEE Internet of Things Journal
Volume10
Issue number19
DOIs
Publication statusPublished - 1 Oct 2023

Keywords

  • Algorithm
  • Haptic Communication
  • Kinesthetic Data
  • LSTM Networks
  • Bilateral Teleoperation

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