Occupant traffic estimation through structural vibration sensing

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.

Covid-19 risk assessment in public transport using ambient sensor data and wireless communications

“…Another device-free occupancy detection solution is presented in (Pan et al, 2016). The main goal of (Pan et al, 2016) is to detect occupancy estimation even for multiple monitored people using vibration sensors. The system can detect the traffic of up to 4 people.…”

“…However, the applicability of the proposed system can be low for mobile systems such as vehicles due to various levels of vibration during mobility. Although the system is proposed for indoor environments, the maximum number of people that can be detected by (Pan et al, 2016) can be limiting for most of the indoor applications. (Shih & Rowe, 2015) employs ultrasonic chirps for indoor occupancy estimation.…”

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

“…These studies usually require high-performance devices or cameras (leading to privacy concerns) to make accurate calculations due to the requirements on the outdoor environments. Indoor examples for this task include using various videos/images such as from a monocular camera on top of a door [17], multiple cameras in smart environments [18,19], infrared and ultrasonic sensors [20,21], Wi-Fi signals [22][23][24][25], RFID [26], structural vibrational sensing [27], CO 2 sensors, and microphones [28]. All these methods might provide good results depending on the environment and fine-tuning, but sacrificing security/privacy of users (camera-based and Wi-Fi based solutions), having high computation overhead (camera-based solutions), or having low accuracy due to low data quality (ultrasonic-, infrared-, and RFID-based solutions, among others).…”

Uncertainties in Structural Behavior for Model-Based Occupant Localization Using Floor Vibrations

“…This has led to ambiguous localization results. Thus, highly instrumented floors are required to provide accurate localization (Bahroun et al, 2014;Pan et al, 2016) (∼one sensor per 2 m 2 ).…”