Proceedings Article | 18 April 2023
KEYWORDS: Sensors, Analog electronics, Data modeling, Windows, Tunable filters, Machine learning, Cameras, Bandpass filters, Linear filtering, Wavelets
In recent years, there has been considerable interest globally in “smart cities,” which aim to improve the performance of urban systems and the experiences of citizens. However, growing interest in smart cities has given rise to many underlying challenges. Society is at a critical juncture where the decisions made to integrate technologies into daily life can either help create an equitable future, or will heighten the inequitable distribution of resources, knowledge, and power in society and infringe on privacy. Nowhere is this more notable than in civil infrastructure systems (e.g., transportation, social infrastructure, the grid, buildings), which are the foundation of society, provide basic public services to communities, and play a critical role in the distribution and usage of energy, goods, and mobility resources. Underpinning the management of many of these civil infrastructure systems is the spatio-temporal tracking of humans and the measurement of human-infrastructure interaction. Already, we have witnessed countless engagements where camera-based sensing systems are designed and deployed to track humans in public spaces. While camera-based solutions often promise to anonymize data by processing video footage using automated data processing tools, many communities are resistant to trust camera-based monitoring due to infringements on privacy and overarching notions of “Big Brother” within the community. Consequently, there is a need to track humans spatially and temporally in a way that minimizes reliance on privacy-invasive sensing technologies (e.g., cameras). In this paper, we design and exploit a human-tracking sensing network that leverages low-power, privacy-preserving passive infrared (PIR) sensors, which merit energy efficiency (i.e., no reliance on fixed/wired energy sources) and full privacy protection. To fill the gaps of existing work, we leverage the analog capabilities of PIR sensors to extract and quantify greater information–—direction of travel, distance relative to the sensor, and speed–—with a single PIR sensor, which would otherwise require multiple PIR sensors operating in a binary fashion in series to gain only partial information. Improved understanding and quantification of the information that can be extracted from the PIR sensor’s signal will pave the way for the development of human tracking networks that can explicitly consider tradeoffs between the value of information gained from a sensor and community-driven privacy concerns. To make this work more accessible by diverse research communities, the electrical software and hardware developed are open-sourced and detailed.
