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Proceedings Paper

Occupant traffic estimation through structural vibration sensing
Author(s): Shijia Pan; Mostafa Mirshekari; Pei Zhang; Hae Young Noh
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Paper Abstract

The number of people passing through different indoor areas is useful in various smart structure applications, including occupancy-based building energy/space management, marketing research, security, etc. Existing approaches to estimate occupant traffic include vision-, sound-, and radio-based (mobile) sensing methods, which have placement limitations (e.g., requirement of line-of-sight, quiet environment, carrying a device all the time). Such limitations make these direct sensing approaches difficult to deploy and maintain. An indirect approach using geophones to measure floor vibration induced by footsteps can be utilized. However, the main challenge lies in distinguishing multiple simultaneous walkers by developing features that can effectively represent the number of mixed signals and characterize the selected features under different traffic conditions. This paper presents a method to monitor multiple persons. Once the vibration signals are obtained, features are extracted to describe the overlapping vibration signals induced by multiple footsteps, which are used for occupancy traffic estimation. In particular, we focus on analysis of the efficiency and limitations of the four selected key features when used for estimating various traffic conditions. We characterize these features with signals collected from controlled impulse load tests as well as from multiple people walking through a real-world sensing area. In our experiments, the system achieves the mean estimation error of ±0.2 people for different occupant traffic conditions (from one to four) using k-nearest neighbor classifier.

Paper Details

Date Published: 20 April 2016
PDF: 12 pages
Proc. SPIE 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016, 980306 (20 April 2016); doi: 10.1117/12.2222024
Show Author Affiliations
Shijia Pan, Carnegie Mellon Univ. (United States)
Mostafa Mirshekari, Carnegie Mellon Univ. (United States)
Pei Zhang, Carnegie Mellon Univ. (United States)
Hae Young Noh, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 9803:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016
Jerome P. Lynch, Editor(s)

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