Real-Time Tracking of Smartwatch Orientation and Location by Multitask Learning

Liu, Miaomiao; Yang, Sikai; Chomsin, Wyssanie; Du, Wan

doi:10.1145/3560905.3568548

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…Classic approaches incorporate external measurements such as GNSS and camera, to periodically correct the propagated pose value, at the cost of additional hardware and power. Recently, learning-aided approaches become popular [5,19,28,30,31,45,46,59]. CO 2 monitoring monitors CO 2 level to ensure occupant comfort.…”

Section: Real-life Sensing Systemsmentioning

confidence: 99%

Physics-Informed Data Denoising for Real-Life Sensing Systems

Zhang,

Fu,

Teng

et al. 2023

Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

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Sensors measuring real-life physical processes are ubiquitous in today's interconnected world. These sensors inherently bear noise that often adversely affects performance and reliability of the systems they support. Classic filtering-based approaches introduce strong assumptions on the time or frequency characteristics of sensory measurements, while learning-based denoising approaches typically rely on using ground truth clean data to train a denoising model, which is often challenging or prohibitive to obtain for many real-world applications. We observe that in many scenarios, the relationships between different sensor measurements (e.g., location and acceleration) are analytically described by laws of physics (e.g., second-order differential equation). By incorporating such physics constraints, we can guide the denoising process to improve even in the absence of ground truth data. In light of this, we design a physics-informed denoising model that leverages the inherent algebraic relationships between different measurements governed by the underlying physics. By obviating the need for ground truth clean data, our method offers a practical denoising solution for realworld applications. We conducted experiments in various domains, including inertial navigation, CO 2 monitoring, and HVAC control, and achieved state-of-the-art performance compared with existing denoising methods. Our method can denoise data in real time (4ms for a sequence of 1s) for low-cost noisy sensors and produces results that closely align with those from high-precision, high-cost alternatives, leading to an efficient, cost-effective approach for more accurate sensor-based systems.

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