Machine Learning for Sensor Transducer Conversion Routines. (arXiv:2108.11374v1 [cs.LG])

Sensors with digital outputs require software conversion routines to
transform the unitless ADC samples to physical quantities with the 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 present 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 for
these methods are 0.0114 $^circ$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 while maintaining good



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