Post date: Mar 29, 2018 7:7:2 PM
Peter Green passed the time on a long train ride by downloading Python onto his smart phone and accessing the device's built-in accelerometer. He found that he could obtain measurements about the train's three-dimensional motion using a convenient Python library of utilities for his Android phone, and imagined that he could use some of the sensors on the phone to do some engineering projects of seismology or vibration mechanics with his undergraduate students. Although they can produce remarkably precise measurements (e.g., Bittel et al. 2016; Mourcou et al. 2015), the accelerometer's precision will depend, presumably, on the brand and particular phone used. The phones are mass produced, so the precision of its sensors would need to be assessed by comparison against a more reliable standard. In practice, the usefulness of the measurement that can be obtained from the device will also depend on the application. Vibrations from handling, extraneous movements, and the working phone itself will likely confound measurements. To use the most convenient implementations of Fourier analysis on the time series data collected by the phone's accelerometer, the inter-sample time step has to be constant, but the data that Peter's Python code collected does not seem to have a constant time step size.
Google Talk on smart phone sensors
https://www.youtube.com/watch?v=C7JQ7Rpwn2k
Bittel, A.J., A. Elazzazi, D.C. Bittel. 2016. Accuracy and precision of an accelerometer-based smartphone app designed to monitor and record angular movement over time. Telemed J E Health 22(4):302-9. doi: 10.1089/tmj.2015.0063. Epub 2015 Oct 8. https://www.ncbi.nlm.nih.gov/pubmed/26447774
Mourcou, Q., A. Fleury, C. Franco, F. Klopcic, and N. Vuillerme. 2015. Performance evaluation of smartphone inertial sensors measurement for range of motion. Sensors (Basel) 15(9): 23168–23187. doi: 10.3390/s150923168, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4610531/#