Machine Learning for Sensor Transducer Conversion Routines
This addresses the need for efficient sensor data processing in resource-constrained embedded systems, though it is incremental as it applies existing ML methods to a specific sensor.
The paper tackled the problem of computationally complex sensor conversion routines for low-power embedded systems by using machine learning to develop less-complex routines for the BME680 sensor, achieving reductions in computational overhead of 62%, 71%, and 18% for temperature, pressure, and humidity respectively with RMS errors of 0.0114°C, 0.0280 KPa, and 0.0337%.
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.