A Multi-Sensor Stochastic Energy-Based Vibro-Localization Technique with Byzantine Sensor Elimination

Ambarkutuk, Murat; Alajlouni, Sa’ed; Tarazaga, Pablo A.; Plassmann, Paul E.

doi:10.3390/s23239309

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…Some of the most advanced techniques for step localization, accounting for localization uncertainty, are presented in [22] , [23] , and [24] . Although these techniques have yet to be tested in unattended scenarios, they offer a probabilistic approach to localization that can enhance gait extraction.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations

MejiaCruz,

Caicedo,

Jiang

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Objective: This research aims to extract human gait parameters from floor vibrations. The proposed approach provides an innovative methodology on occupant activity, contributing to a broader understanding of how human movements interact within their built environment.Methods and Procedures: A multilevel probabilistic model was developed to estimate cadence and walking speed through the analysis of floor vibrations induced by walking. The model addresses challenges related to missing or incomplete information in the floor acceleration signals. Following the Bayesian Analysis Reporting Guidelines (BARG) for reproducibility, the model was evaluated through twenty-seven walking experiments, capturing floor vibration and data from Ambulatory Parkinson’s Disease Monitoring (APDM) wearable sensors. The model was tested in a real-time implementation where ten individuals were recorded walking at their own selected pace.Results: Using a rigorous combined decision criteria of 95% high posterior density (HPD) and the Range of Practical Equivalence (ROPE) following BARG, the results demonstrate satisfactory alignment between estimations and target values for practical purposes. Notably, with over 90% of the 95% HPD falling within the region of practical equivalence, there is a solid basis for accepting the estimations as probabilistically aligned with the estimations using the APDM sensors and video recordings.Conclusion: This research validates the probabilistic multilevel model in estimating cadence and walking speed by analyzing floor vibrations, demonstrating its satisfactory comparability with established technologies such as APDM sensors and video recordings. The close alignment between the estimations and target values emphasizes the approach’s efficacy. The proposed model effectively tackles prevalent challenges associated with missing or incomplete data in real-world scenarios, enhancing the accuracy of gait parameter estimations derived from floor vibrations.Clinical impact: Extracting gait parameters from floor vibrations could provide a non-intrusive and continuous means of monitoring an individual’s gait, offering valuable insights into mobility and potential indicators of neurological conditions. The implications of this research extend to the development of advanced gait analysis tools, offering new perspectives on assessing and understanding walking patterns for improved diagnostics and personalized healthcare.Clinical and Translational Impact Statement: This manuscript introduces an innovative approach for unattended gait assessments with potentially significant implications for clinical decision-making. By utilizing floor vibrations to estimate cadence and walking speed, the technology can provide clinicians with valuable insights into their patients’ mobility and functional abilities in real-life settings. The strategic installation of accelerometers beneath the flooring of homes or care facilities allows for uninterrupted daily activities during these assessments, reducing the reliance on specialize...

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Section: Introductionmentioning

confidence: 99%

Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations

MejiaCruz,

Caicedo,

Jiang

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Machine learning-based gait health monitoring for multi-occupant smart homes

Hahm,

Anthony

2024

Internet of Things

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Modeling and Analysis of Dispersive Propagation of Structural Waves for Vibro-Localization

Ambarkutuk,

Plassmann

2024

Sensors

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The dispersion of structural waves, where wave speed varies with frequency, introduces significant challenges in accurately localizing occupants in a building based on vibrations caused by their movements. This study presents a novel multi-sensor vibro-localization technique that accounts for dispersion effects, enhancing the accuracy and robustness of occupant localization. The proposed method utilizes a model-based approach to parameterize key propagation phenomena, including wave dispersion and attenuation, which are fitted to observed waveforms. The localization is achieved by maximizing the joint likelihood of the occupant’s location based on sensor measurements. The effectiveness of the proposed technique is validated using two experimental datasets: one from a controlled environment involving an aluminum plate and the other from a building-scale experiment conducted at Goodwin Hall, Virginia Tech. Results for the proposed algorithm demonstrates a significant improvement in localization accuracy compared to benchmark algorithms. Specifically, in the aluminum plate experiments, the proposed technique reduced the average localization precision from 7.77 cm to 1.97 cm, representing a ∼74% improvement. Similarly, in the Goodwin Hall experiments, the average localization error decreased from 0.67 m to 0.3 m, with a ∼55% enhancement in accuracy. These findings indicate that the proposed approach outperforms existing methods in accurately determining occupant locations, even in the presence of dispersive wave propagation.

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A Multi-Sensor Stochastic Energy-Based Vibro-Localization Technique with Byzantine Sensor Elimination

Cited by 3 publications

References 45 publications

Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations

Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations

Machine learning-based gait health monitoring for multi-occupant smart homes

Modeling and Analysis of Dispersive Propagation of Structural Waves for Vibro-Localization

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