Robust Occupant Detection Through Step-Induced Floor Vibration by Incorporating Structural Characteristics

Lam, Mike; Mirshekari, Mostafa; Pan, Shijia; Zhang, Pei; Noh, Hae Young

doi:10.1007/978-3-319-29763-7_35

Cited by 26 publications

(14 citation statements)

References 24 publications

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“…Acoustic Event Detection: Artificial neural networks have been applied to acoustic event classification [8,9,19,41] which includes among others footstep detection. Also footstep detection and person identification using geophones has been studied before [3,21,30], however only in experiments in a controlled environment, not on embedded devices or using additional structural information. Artificial neural networks have been recently applied to seismic event detection [29].…”

Section: Related Workmentioning

confidence: 99%

“…Here, several challenges need to be addressed. Multiple footstep detectors using geophones have been proposed [3,30] but have not been shown to distinguish well between footsteps and seismic events [27] or require further structural information [21]. Convolutional neural networks have shown to be good signal processing tools for classification of acoustic [19] as well as seismic sources [32].…”

Section: Classification With Time Distributed Processingmentioning

confidence: 99%

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Event-triggered natural hazard monitoring with convolutional neural networks on the edge

Meyer

Farei-Campagna

Pasztor

et al. 2019

Proceedings of the 18th International Conference on Information Processing in Sensor Networks

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In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of eventtriggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly and efficiently at times when it matters most and consequentially not wasting precious resources when nothing can be observed. On the other hand we utilize machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by quantizing and pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices. A field study with our system is running on the rockfall scarp of the Matterhorn Hörnligrat at 3500 m a.s.l. since 08/2018.

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Section: Related Workmentioning

confidence: 99%

Section: Classification With Time Distributed Processingmentioning

confidence: 99%

Event-triggered natural hazard monitoring with convolutional neural networks on the edge

Meyer

Farei-Campagna

Pasztor

et al. 2019

Proceedings of the 18th International Conference on Information Processing in Sensor Networks

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show abstract

“…This compactness results in easier event detection because fewer features can represent the event of interest. Wavelet analysis has been widely applied as a promising tool to extract structural dynamic characteristics in structural health monitoring and other related fields (Chang, 1999;Hera and Hou, 2004;Taha et al, 2006;Noh et al, , 2011Noh et al, , 2012Mirshekari et al, 2015Mirshekari et al, , 2016aPan et al, 2015aPan et al, ,b, 2016Lam et al, 2016). Similarly, we use wavelet to extract structural dynamic characteristics that change with train activities.…”

Section: Extract Wavelet-based Featuresmentioning

confidence: 99%

An Information-Theoretic Approach for Indirect Train Traffic Monitoring Using Building Vibration

Zhang

et al. 2017

Front. Built Environ.

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This paper introduces an indirect train traffic monitoring method to detect and infer real-time train events based on the vibration response of a nearby building. Monitoring and characterizing traffic events are important for cities to improve the efficiency of transportation systems (e.g., train passing, heavy trucks, and traffic). Most prior work falls into two categories: (1) methods that require intensive labor to manually record events or (2) systems that require deployment of dedicated sensors. These approaches are difficult and costly to execute and maintain. In addition, most prior work uses dedicated sensors designed for a single purpose, resulting in deployment of multiple sensor systems. This further increases costs. Meanwhile, with the increasing demands of structural health monitoring, many vibration sensors are being deployed in commercial buildings. Traffic events create ground vibration that propagates to nearby building structures inducing noisy vibration responses. We present an information-theoretic method for train event monitoring using commonly existing vibration sensors deployed for building health monitoring. The key idea is to represent the wave propagation in a building induced by train traffic as information conveyed in noisy measurement signals. Our technique first uses wavelet analysis to detect train events. Then, by analyzing information exchange patterns of building vibration signals, we infer the category of the events (i.e., southbound or northbound train). Our algorithm is evaluated with an 11-story building where trains pass by frequently. The results show that the method can robustly achieve a train event detection accuracy of up to a 93% true positive rate and an 80% true negative rate. For direction categorization, compared with the traditional signal processing method, our information-theoretic approach reduces categorization error from 32.1 to 12.1%, which is a 2.5× improvement.

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“…This approach has been shown feasible in other applications, including person identification, 29 person tracking/locating 19,21,22 and person-by-person occupancy estimation. 17,27 However, prior work has focused on only single person walking scenarios, which may not be assumed in general. 17,19,21,27,29 In this paper, we present a method to monitor multiple occupant traffic through sensing the ambient structural vibration.…”

Section: Introductionmentioning

confidence: 99%

“…17,27 However, prior work has focused on only single person walking scenarios, which may not be assumed in general. 17,19,21,27,29 In this paper, we present a method to monitor multiple occupant traffic through sensing the ambient structural vibration. Our system achieves occupant traffic monitoring by acquiring signals from structural vibration sensors and analyzing their features.…”

Section: Introductionmentioning

confidence: 99%

Occupant traffic estimation through structural vibration sensing

et al. 2016

Self Cite

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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.

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Robust Occupant Detection Through Step-Induced Floor Vibration by Incorporating Structural Characteristics

Cited by 26 publications

References 24 publications

Event-triggered natural hazard monitoring with convolutional neural networks on the edge

Event-triggered natural hazard monitoring with convolutional neural networks on the edge

An Information-Theoretic Approach for Indirect Train Traffic Monitoring Using Building Vibration

Occupant traffic estimation through structural vibration sensing

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