Packet Loss Concealment with Recurrent Neural Networks for Wireless Inertial Pose Tracking

Xuesu Xiao1, Shuayb Zarar2

  • 1Texas A&M University
  • 2Microsoft Research



Contributed Paper (Oral)


11:20 - 12:50 | Mon 5 Mar | Antilles CD | MoBT1.1

BSN Session # 2 – Gait, Motion and Activity Monitoring


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 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%.

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