ABSTRACT
Human foot traffic in urban environments provides essential information for city planners to manage the urban resources and urban residents to plan their activities. Compared to camera or mobile-based solutions, seismic sensors detect human footstep signals with fewer privacy concerns. However, seismic sensors often record signals generated from multiple sources, particularly in an urban outdoor environment. In this article, we monitor people's running activities during COVID-19 pandemic with a seismic sensor in a park in Singapore. We compare the spectra of natural and urban events in the recorded seismic data. For each 3 s seismic data, we define hierarchical screening criteria to identify footsteps based on the spectrum of the signal and its envelope. We derive the cadence of each runner by detecting the primary frequency of the footstep signals. The resulting algorithm achieves higher accuracy and higher temporal resolution for weak and overlapping signals compared to existing methods. Runner statistics based on four-month long seismic data show that urban running activities have clear daily and weekly cycles. Lockdown measures to mitigate COVID-19 pandemic promoted running activities, particularly over the weekends. Cadence statistics show that morning runners have higher cadence on average.