@inproceedings{xiao2018packet,
author = {Xiao, Xuesu and Zarar, Shuayb},
title = {Packet Loss Concealment with Recurrent Neural Networks for Wireless Inertial Pose Tracking},
booktitle = {IEEE Int. Conf. Wearable and Implantable Body Sensor Networks (BSN)},
year = {2018},
month = {March},
abstract = {Inertial sensing is a technology that enables motion capture outside of well-defined studio environments. Yet, there are several hurdles that have to be overcome in order to achieve a high-quality user experience. Among them is enabling robust wireless communication. Thanks to strict requirements on throughput and far-field operation along with existing issues of occlusion and client interference, packet-loss rates in wireless inertial-sensing systems can amplify pose-tracking errors by as much as 39%. In this paper, we develop a new type of sequence-predictors based on long short-term memory recurrent neural networks that can be used to significantly conceal packet losses for inertial pose-tracking. To lower computational overheads, we systematically exploit spatio-temporal correlations of data and distribute sensor loads among multiple predictors. Through experiments conducted with 3.5 hrs. of high-frequency inertial motion-capture data, we demonstrate that our approach is able to fully conceal packet losses at rates of up to 20%.},
url = {http://approjects.co.za/?big=en-us/research/publication/packet-loss-concealment-wireless-networks-lstm-sequence-predictors-inertial-pose-tracking/},
edition = {IEEE Int. Conf. Wearable and Implantable Body Sensor Networks (BSN)},
}