While it may be attractive to view sensors as simple transducers
which convert physical quantities into electrical signals, the
truth of the matter is more complex. The engineer should have a
proper understanding of the physics involved in the conversion
process, including interactions with other measurable quantities. A
deep understanding of these interactions can be leveraged to apply
sensor fusion techniques to minimize noise and/or extract
additional information from sensor signals. Advances in
microcontroller and MEMS manufacturing, along with improved
internet connectivity, have enabled cost-effective wearable and
Internet of Things sensor applications. At the same time, machine
learning techniques have gone mainstream, so that those same
applications can now be more intelligent than ever before. This
book explores these topics in the context of a small set of sensor
types. We provide some basic understanding of sensor operation for
accelerometers, magnetometers, gyroscopes, and pressure sensors. We
show how information from these can be fused to provide estimates
of orientation. Then we explore the topics of machine learning and
sensor data analytics.
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