Large scale indoor occupant tracking using distributed acoustic sensing and machine learning

Abstract

The recent COVID-19 pandemic has brought attention to the criticality of implementing measures to prevent viral transmission and reduce the risk of infection. It is necessary to identify crowd-gathering hazards and implement appropriate engineering control measures. Consequently, obtaining comprehensive footfall information at a large scale becomes significant. Vibration-based sensors are currently the preferred choice for footfall detection, with geophones being the commonly utilized sensors known for their effective performance. However, the deployment of geophones in large-scale scenarios presents challenges due to the substantial amount of manual labor involved. To address these limitations, the Distributed Acoustic Sensing (DAS) system, a non-intrusive sensor, is introduced as an alternative solution. While DAS has been successfully applied in various long-distance scenarios, such as pipeline surveillance and real-time train tracking, its effectiveness in footfall detection requires further evaluation. Therefore, this study proposes a machine learning method to achieve footstep recognition using DAS, considering the inherent time dependency and spatial continuity present in footfall trajectories. To further evaluate the effectiveness of DAS, geophones are selected as the benchmark sensors. The evaluation process encompasses footstep recognition and additional data implementation. The results demonstrate that the proposed recognition method achieves high accuracy for both sensors, thereby establishing DAS as a more applicable solution for footfall detection compared to geophones. In conclusion, DAS offers the advantage of flexible space resolution settings and adaptable deployment options, rendering it suitable for applications in diverse scenarios. Its capabilities make it suitable for large-scale occupancy monitoring scenarios.