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\u00a0for 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%.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13552,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-449847","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-hardware-devices","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"","msr_edition":"IEEE Int. Conf. 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