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Probabilistic Force Estimation and Event Localization (PFEEL) algorithm
https://orcid.org/0000-0003-3657-4850
https://orcid.org/0000-0002-4931-1622
https://orcid.org/0000-0001-6409-231X
https://orcid.org/0000-0002-8565-4534
Abstract Localization of human activity using floor vibrations has gained attention in recent years. In human health technologies, floor vibrations have been recently used to estimate gait parameters to predict a patients’ health status. Various methodologies such as using the characteristics of wave traveling (algorithms based on time of arrival) or the properties of structures (Force Estimation and Event Localization, FEEL, algorithm) have been investigated to localize the impact, fall, or step events. This paper presents a probabilistic approach that builds upon the FEEL algorithm to offer the advantage of eliminating the need for a robust experimental setup. The proposed Probabilistic Force Estimation and Event Localization (PFEEL) algorithm provides a probabilistic measure to an event’s force estimation and localization using random variables associated with the floor’s dynamics. The algorithm can also guide calibration by identifying calibration points that provide the maximum information. This reduces the number of calibration points needed, which has practical benefits during the implementation. In this manuscript, we presented the design, development, and validation of the algorithm.
Highlights Probabilistic measure of the localization and force magnitude of an event using floor vibrations. Modeling transfer functions between impact and response locations as a hyper-surface, continuous in the space. Bayesian framework to model the response of a system in a probabilistic fashion. PFEEL can be used to guide the calibration process to minimize the uncertainty in the estimations.
Probabilistic Force Estimation and Event Localization (PFEEL) algorithm
https://orcid.org/0000-0003-3657-4850
https://orcid.org/0000-0002-4931-1622
https://orcid.org/0000-0001-6409-231X
https://orcid.org/0000-0002-8565-4534
Abstract Localization of human activity using floor vibrations has gained attention in recent years. In human health technologies, floor vibrations have been recently used to estimate gait parameters to predict a patients’ health status. Various methodologies such as using the characteristics of wave traveling (algorithms based on time of arrival) or the properties of structures (Force Estimation and Event Localization, FEEL, algorithm) have been investigated to localize the impact, fall, or step events. This paper presents a probabilistic approach that builds upon the FEEL algorithm to offer the advantage of eliminating the need for a robust experimental setup. The proposed Probabilistic Force Estimation and Event Localization (PFEEL) algorithm provides a probabilistic measure to an event’s force estimation and localization using random variables associated with the floor’s dynamics. The algorithm can also guide calibration by identifying calibration points that provide the maximum information. This reduces the number of calibration points needed, which has practical benefits during the implementation. In this manuscript, we presented the design, development, and validation of the algorithm.
Highlights Probabilistic measure of the localization and force magnitude of an event using floor vibrations. Modeling transfer functions between impact and response locations as a hyper-surface, continuous in the space. Bayesian framework to model the response of a system in a probabilistic fashion. PFEEL can be used to guide the calibration process to minimize the uncertainty in the estimations.
Probabilistic Force Estimation and Event Localization (PFEEL) algorithm
https://orcid.org/0000-0003-3657-4850
https://orcid.org/0000-0002-4931-1622
https://orcid.org/0000-0001-6409-231X
https://orcid.org/0000-0002-8565-4534
Abstract Localization of human activity using floor vibrations has gained attention in recent years. In human health technologies, floor vibrations have been recently used to estimate gait parameters to predict a patients’ health status. Various methodologies such as using the characteristics of wave traveling (algorithms based on time of arrival) or the properties of structures (Force Estimation and Event Localization, FEEL, algorithm) have been investigated to localize the impact, fall, or step events. This paper presents a probabilistic approach that builds upon the FEEL algorithm to offer the advantage of eliminating the need for a robust experimental setup. The proposed Probabilistic Force Estimation and Event Localization (PFEEL) algorithm provides a probabilistic measure to an event’s force estimation and localization using random variables associated with the floor’s dynamics. The algorithm can also guide calibration by identifying calibration points that provide the maximum information. This reduces the number of calibration points needed, which has practical benefits during the implementation. In this manuscript, we presented the design, development, and validation of the algorithm.
Highlights Probabilistic measure of the localization and force magnitude of an event using floor vibrations. Modeling transfer functions between impact and response locations as a hyper-surface, continuous in the space. Bayesian framework to model the response of a system in a probabilistic fashion. PFEEL can be used to guide the calibration process to minimize the uncertainty in the estimations.
Probabilistic Force Estimation and Event Localization (PFEEL) algorithm
MejiaCruz, Yohanna (author) / Jiang, Zhaoshuo (author) / Caicedo, Juan M. (author) / Franco, Jean M. (author)
Engineering Structures ; 252
2021-10-30
Article (Journal)
Electronic Resource
English
Probabilistic Force Estimation and Event Localization (PFEEL) algorithm
| Elsevier | 2022
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