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Machine Learning for Sensor Transducer Conversion Routines

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Newton, Thomas 
Stanley-Marbell, Phillip  ORCID logo  https://orcid.org/0000-0001-7752-2083

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.

Description

Keywords

Journal Title

IEEE Embedded Systems Letters

Conference Name

Journal ISSN

1943-0663

Volume Title

Publisher

Institute of Electrical and Electronics Engineers
Sponsorship
EPSRC (2084773)
Alan Turing Institute (EP/N510129/1)
EPSRC (EP/V047507/1)
TU/B/000096 EP/N510129/1 EP/V047507/1 EP/V061798/1