Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2020

Abstract

Natural human interactions for Mixed Reality Applications are overwhelmingly multimodal: humans communicate intent and instructions via a combination of visual, aural and gestural cues. However, supporting low-latency and accurate comprehension of such multimodal instructions (MMI), on resource-constrained wearable devices, remains an open challenge, especially as the state-of-the-art comprehension techniques for each individual modality increasingly utilize complex Deep Neural Network models. We demonstrate the possibility of overcoming the core limitation of latency--vs.--accuracy tradeoff by exploiting cross-modal dependencies -- i.e., by compensating for the inferior performance of one model with an increased accuracy of more complex model of a different modality. We present a sensor fusion architecture that performs MMI comprehension in a quasi-synchronous fashion, by fusing visual, speech and gestural input. The architecture is reconfigurable and supports dynamic modification of the complexity of the data processing pipeline for each individual modality in response to contextual changes. Using a representative "classroom" context and a set of four common interaction primitives, we then demonstrate how the choices between low and high complexity models for each individual modality are coupled. In particular, we show that (a) a judicious combination of low and high complexity models across modalities can offer a dramatic 3-fold decrease in comprehension latency together with an increase 10-15% in accuracy, and (b) the right collective choice of models is context dependent, with the performance of some model combinations being significantly more sensitive to changes in scene context or choice of interaction.

Keywords

sensor fusion, mixed reality, multimodal interactions

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2020 17th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS): Delhi, India, 10-13 December: Proceedings

First Page

309

Last Page

317

ISBN

9781728198668

Identifier

10.1109/MASS50613.2020.00046

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

2-14-2021

Copyright Owner and License

LARC

Additional URL

https://doi.org/10.1109/MASS50613.2020.00046

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