An interesting question will be which combinations of data may provide the most accurate findings. JL
Kyle Wiggers reports in Venture Beat:
To train machine learning models capable of distinguishing between steps (and by extension, people), the researchers collected both the time and frequency of footfalls in addition to their length and cadence (the gap between two consecutive footsteps). Over the period of a month, they used a geophone to collect roughly 46,000 footfall events from eight barefooted test participants. They posit that in the real world, data collection would be best accomplished by dividing a “monitoring area” — e.g., a college or factory — into “zones.