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.
About the Author
Michael Stanley received his B.S.E. from Michigan State University in 1980 and his M.S. from Arizona State University in 1986. His career spans over three decades at Motorola Semiconductor, Freescale Semiconductor, and NXP Semiconductor. He is a Senior Member of the IEEE and is listed as inventor or co-inventor on seven patents. Mike was a contributor to the IEEE Standard for Sensor Performance Parameter Definitions (IEEE Std 2700-2014) as well as the 2nd edition of the Measurements, Instrumentation, and Sensors Handbook by CRC Press. He was inducted into the MEMS & Sensors Industry Group Hall of Fame in 2015 and he is an active member of the Industry Advisory Board for the Sensor, Signal, and Information Processing Center (SenSIP) at Arizona State University.Jongmin Lee is a Systems & Architecture Engineer at NXP Semiconductor. He received his Ph.D. in Electrical Engineering from Arizona State University in 2017. Previously, he was a graduate research associate at the Sensor, Signal, and Information Processing Center (SenSIP) at Arizona State University. His research interests include signal processing, communications, and machine learning.