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arXiv · 2108.11374

Machine Learning for Sensor Transducer Conversion Routines

Thomas Newton·James T. Meech·Phillip Stanley-Marbell
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Abstract

Sensors with digital outputs require software conversion routines to transform the unitless analogue-to-digital converter samples to physical quantities with correct units. These conversion routines are computationally complex given the limited computational resources of low-power embedded systems. This article presents a set of machine learning methods to learn new, less-complex conversion routines that do not sacrifice accuracy for the BME680 environmental sensor. We present a Pareto analysis of the tradeoff between accuracy and computational overhead for the models and models that reduce the computational overhead of the existing industry-standard conversion routines for temperature, pressure, and humidity by 62%, 71 %, and 18 % respectively. The corresponding RMS errors are 0.0114 degrees C, 0.0280 KPa, and 0.0337 %. These results show that machine learning methods for learning conversion routines can produce conversion routines with reduced computational overhead which maintain good accuracy.

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Thomas Newton, James T. Meech, Phillip Stanley-Marbell. 2021-11-08. Machine Learning for Sensor Transducer Conversion Routines. https://arxiv.org/abs/2108.11374

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