Vibration-Based Occupant Detection Using a Multiple-Model Approach

Reuland, Yves; Pai, Sai Ganesh Sarvotham; Drira, Slah; Smith, Ian F. C.

doi:10.1007/978-3-319-54777-0_6

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Using the initial location set, for each detected footstep event (e), all location instances whose residuals lie inside the localization thresholds (T low,i,e and T high,i,e ) at each sensor location (i) are accepted and form the candidate-location set (CLS) (Reuland et al, 2017b;Drira et al, 2019b;Drira et al, 2019c;Drirs et al, 2019d) as shown in Eq. 5.…”

Section: Error-domain Model Falsification (Edmf)mentioning

confidence: 99%

“…Use of physics-based models introduces knowledge of structural behavior while interpreting data and may overcome shortcomings of current data-driven techniques. A model-based approach using error-domain model falsification (EDMF) has been shown to provide accurate localization (Reuland et al, 2017b;Drira et al, 2019b;Drira et al, 2019c;Drira et al, 2019d) and precise tracking of a single occupant (Drira et al, 2019b;Drira et al, 2019c;Drira et al, 2019d). EDMF is a population-based approach that explicitly accommodates systematic errors and model bias to provide accurate identification.…”

Section: Introductionmentioning

confidence: 99%

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

Drira

Pai

Smith

2021

Front. Built Environ.

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In sensed buildings, information related to occupant movement helps optimize functions such as security, energy management, and caregiving. Due to privacy needs, non-intrusive sensing approaches for tracking occupants inside buildings, such as vibration sensors, are often preferred over intrusive strategies that involve use of cameras and wearable devices. Current sensor-based occupant-localization approaches are data-driven techniques that do not account for structural behavior and limited to slabs on grade. Varying-rigidity floors and inherent variability in walking gaits lead to ambiguous interpretations of floor vibrations when performing model-free occupant localization. In this paper, an extensive analysis of vibrations induced by a range of occupants is described. Firstly, the need for a structural-behavior model for occupant localization is assessed using two full-scale case studies. Structural behavior is found to significantly influence floor vibrations induced by footstep impacts. Since a simple relationship between distances from footstep-impact to sensor locations cannot be assured, the use of physics-based models is necessary for accurate occupant localization. Secondly, measured data are interpreted using physics-based models and information related to uncertainties from multiple sources. There are two types of uncertainties: modelling uncertainties and measurement uncertainties, including variability in walking gaits. Error-domain model falsification (EDMF) and residual minimization (RM) are model-based approaches for data interpretation. Unlike RM, EDMF explicitly accounts for the presence of systematic errors in parameters and overall model bias. In this paper, model-based occupant localization is carried out using EDMF and RM on a full-scale case study. By explicitly accounting for the presence of uncertainties and the influence of structural behavior, EDMF, unlike RM, accurately reveals possible occupant locations on floor slabs.

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Section: Error-domain Model Falsification (Edmf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Drira

Pai

Smith

2021

Front. Built Environ.

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“…The EDMF methodology has been applied to more than 20 full-scale systems since 1998 [4]. Recent applications include: model identification [70]; leak detection [71,72]; wind simulation [73]; fatigue life evaluation [74][75][76]; measurement-system design [77][78][79][80]; post-earthquake assessment [34,81]; damage localization in tensegrity structures [82]; and occupant localization [83].…”

Section: Error-domain Model Falsificationmentioning

confidence: 99%

Data-Interpretation Methodologies for Practical Asset-Management

Pai

Reuland

Smith

2019

JSAN

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Monitoring and interpreting structural response using structural-identification methodologies improves understanding of civil-infrastructure behavior. New sensing devices and inexpensive computation has made model-based data interpretation feasible in engineering practice. Many data-interpretation methodologies, such as Bayesian model updating and residual minimization, involve strong assumptions regarding uncertainty conditions. While much research has been conducted on the scientific development of these methodologies and some research has evaluated the applicability of underlying assumptions, little research is available on the suitability of these methodologies to satisfy practical engineering challenges. For use in practice, data-interpretation methodologies need to be able, for example, to respond to changes in a transparent manner and provide accurate model updating at minimal additional cost. This facilitates incremental and iterative increases in understanding of structural behavior as more information becomes available. In this paper, three data-interpretation methodologies, Bayesian model updating, residual minimization and error-domain model falsification, are compared based on their ability to provide robust, accurate, engineer-friendly and computationally inexpensive model updating. Comparisons are made using two full-scale case studies for which multiple scenarios are considered, including incremental acquisition of information through measurements. Evaluation of these scenarios suggests that, compared with other data-interpretation methodologies, error-domain model falsification is able to incorporate, iteratively and transparently, incremental information gain to provide accurate model updating at low additional computational cost.

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Study on Coupled Vibration of Human-plate System under Pedestrian Excitation

Cao,

Wang,

et al. 2024

KSCE J Civ Eng

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Vibration-Based Occupant Detection Using a Multiple-Model Approach

Cited by 9 publications

References 15 publications

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

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

Data-Interpretation Methodologies for Practical Asset-Management

Study on Coupled Vibration of Human-plate System under Pedestrian Excitation

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