Indoor Person Identification through Footstep Induced Structural Vibration

Pan, Shijia; Wang, Ningning; Qian, Ying; Velibeyoglu, Irem; Noh, Hae Young; Zhang, Pei

doi:10.1145/2699343.2699364

Cited by 112 publications

(51 citation statements)

References 15 publications

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“…In addition to obvious remedies such as improving sensor density there may be an algorithmic remedy requiring no additional sensor infrastructure. As noted in the introduction, prior literature (Pan et al, 2015;Bales et al, 2016) extracted statistical features from footstep measurements that enable discrimination among individuals beyond the location and time parameters considered in this paper. Additionally, if the algorithms undertake actual tracking of occupants-not just occupancy tracking-the accuracy has the potential to improve, because the algorithms incorporate all information from a set of observed footsteps and per person state variables (e.g., velocity).…”

Section: Discussionmentioning

confidence: 99%

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A Framework for Occupancy Tracking in a Building via Structural Dynamics Sensing of Footstep Vibrations

Poston

Buehrer

Tarazaga

2017

Front. Built Environ.

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Counting the number of occupants in building areas over time-occupancy tracking-provides valuable information for responding to emergencies, optimizing thermal conditions or managing personnel. This capability is distinct from tracking individual building occupants as they move within a building, has lower complexity than conventional tracking algorithms require, and avoids privacy concerns that tracking individuals may pose. The approach proposed here is a novel combination of data analytics applied to measurements from a building's structural dynamics sensors (e.g., accelerometers or geophones). Specifically, measurements of footstep-generated structural waves provide evidence of occupancy in a building area. These footstep vibrations can be distinguished from other vibrations, and, once identified, the footsteps can be located. These locations, in turn, form the starting point of estimating occupancy in an area. In order to provide a meaningful occupancy count, however, it is first necessary to associate discrete footsteps with individuals. The proposed framework incorporates a tractable algorithm for this association task. The proposed algorithms operate online, updating occupancy count over time as new footsteps are detected. Experiments with measurements from a public building illustrate the operation of the proposed framework. This approach offers an advantage over others based on conventional technologies by avoiding the cost of a separate sensor system devoted to occupancy tracking.

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Section: Discussionmentioning

confidence: 99%

“…Furthermore, extracting features from the footstep measurements and applying the features to statistical models of human gait shows promise for distinguishing among individuals by their characteristic gait (Pan et al, 2015) or determining gender (Bales et al, 2016).…”

Section: Research Motivationmentioning

confidence: 99%

A Framework for Occupancy Tracking in a Building via Structural Dynamics Sensing of Footstep Vibrations

Poston

Buehrer

Tarazaga

2017

Front. Built Environ.

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“…Works have been done on human information monitoring through vibration induced by their activities, including identity (Ekimov and Sabatier, 2006;Itai and Yasukawa, 2008;Pan et al, 2015), gender (Bales et al, 2016a,b), location (Mirshekari et al, 2015Poston et al, 2015;Schloemann et al, 2015), trajectory (Dobbler et al, 2014;Pan et al, 2014), traffic (Subramanian et al, 2010;Pan et al, 2016), and activity (Pan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…A large amount of research has been focusing on feature extraction and information learning for different vibration-based applications (Dobbler et al, 2014;Mirshekari et al, 2015Mirshekari et al, , 2016Pan et al, 2015Pan et al, , 2016Bales et al, 2016b). However, if the raw signals acquired are already distorted (signal clipping) or of low resolution, the learning can hardly compensate for such information loss.…”

Section: Introductionmentioning

confidence: 99%

Collaboratively Adaptive Vibration Sensing System for High-fidelity Monitoring of Structural Responses Induced by Pedestrians

Pan

Mirshekari

et al. 2017

Front. Built Environ.

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This paper presents a collaboratively adaptive vibration monitoring system that captures high-fidelity structural vibration signals induced by pedestrians. These signals can be used for various human activities' monitoring by inferring information about the impact sources, such as pedestrian footsteps, door opening and closing, and dragging objects. Such applications often require high-fidelity (high resolution and low distortion) signals. Traditionally, expensive high resolution and high dynamic range sensors are adopted to ensure sufficient resolution. However, for sensing systems that use low-cost sensing devices, the resolution and dynamic range are often limited; hence this type of sensing methods is not well explored ubiquitously. We propose a low-cost sensing system that utilizes (1) a heuristic model of the investigating excitations and (2) shared information through networked devices to adapt hardware configurations and obtain high-fidelity structural vibration signals. To further explain the system, we use indoor pedestrian footstep sensing through ambient structural vibration as an example to demonstrate the system performance. We evaluate the application with three metrics that measure the signal quality from different aspects: the sufficient resolution rate to present signal resolution improvement without clipping, the clipping rate to measure the distortion of the footstep signal, and the signal magnitude to quantify the detailed resolution of the detected footstep signal. In experiments conducted in a school building, our system demonstrated up to 2× increase on the sufficient resolution rate and 2× less error rate when used to locate the pedestrians as they walk along the hallway, compared to a fixed sensing setting.

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“…More recent approaches utilize occupant footstep induced floor vibrations to infer pedestrians, 19,21,22,27,29 which can also be used as the indicator of the occupant traffic. Monitoring the occupant traffic indirectly through structural vibrations overcomes the installation barrier and sensing limitations faced by traditional device-free techniques.…”

Section: Introductionmentioning

confidence: 99%

Occupant traffic estimation through structural vibration sensing

et al. 2016

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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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Indoor Person Identification through Footstep Induced Structural Vibration

Cited by 112 publications

References 15 publications

A Framework for Occupancy Tracking in a Building via Structural Dynamics Sensing of Footstep Vibrations

A Framework for Occupancy Tracking in a Building via Structural Dynamics Sensing of Footstep Vibrations

Collaboratively Adaptive Vibration Sensing System for High-fidelity Monitoring of Structural Responses Induced by Pedestrians

Occupant traffic estimation through structural vibration sensing

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