We study the properties of inertial signals, principally those produced by the motion of accelerometer sensors. This is a path leading, for students and researchers alike, to a deeper understanding of the connections between the most fundamental notions in classical physics (force, acceleration, inertia, gravity) and some of their expressions in the context of modern physics (space-time and Einstein’s equivalence principle, quantum accelerometry).
Such an investigation naturally leads to the exploration of the inverse problem, viz. the ability to infer motion from inertial signals. Combined with modern data-analysis paradigms such as multi-variate optimization and machine learning, inertial devices have an astounding potential to reveal information about the object to which they are attached, be it a spacecraft, your car, or the movement of your own body.
The fruits of our work include the development of a variety of pedagogical resources including 3D-printed apparatus and experiments using the accelerometer sensors found in smartphones.
** See our latest student research stipends for W2023 **
Git Repository: https://github.com/larnder/2019_06_AccelerationCamp.git