Probabilistic Estimation of Cadence and Walking Speed From Floor Vibrations.

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 specialized clinical environments. This technology enables continuous monitoring of gait patterns over time and has the potential for integration into healthcare platforms. Such integration can enhance remote monitoring, leading to timely interventions and personalized care plans, ultimately improving clinical outcomes. The probabilistic nature of our model enables uncertainty quantification in the estimated parameters, providing clinicians with a nuanced understanding of data reliability.

Probabilistic detection of impacts using the PFEEL algorithm with a Gaussian Process Regression Model.

Methods for identifying human activity have a wide range of potential applications, including security, event time detection, intelligent building environments, and human health. Current methodologies typically rely on either wave propagation or structural dynamics principles. However, force-based methods, such as the probabilistic force estimation and event localization algorithm (PFEEL), offer advantages over wave propagation methods by avoiding challenges such as multi-path fading. PFEEL utilizes a probabilistic framework to estimate the force of impacts and the event locations in the calibration space, providing a measure of uncertainty in the estimations. This paper presents a new implementation of PFEEL using a data-driven model based on Gaussian process regression (GPR). The new approach was evaluated using experimental data collected on an aluminum plate impacted at eighty-one points, with a separation of five centimeters. The results are presented as an area of localization relative to the actual impact location at different probability levels. These results can aid analysts in determining the required precision for various implementations of PFEEL.

Probabilistic Force Estimation and Event Localization (PFEEL) algorithm.

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.

Walking speed measurement technology: A review.

PURPOSE OF REVIEW: This article presents an overview of the main technologies used to estimate gait parameters, focusing on walking speed (WS). RECENT FINDINGS: New wearable and environmental technologies to estimate WS have been developed in the last five years. Wearable technologies refer to sensors attached to parts of the patient's body that capture the kinematics during walking. Alternatively, environmental technologies capture walking patterns using external instrumentation. In this review, wearable and external technologies have been included.From the different works reviewed, external technologies face the challenge of implementation outside controlled facilities; an advantage that wearable technologies have, but have not been fully explored. Additionally, systems that can track WS changes in daily activities, especially at-home assessments, have not been developed. SUMMARY: Walking speed is a gait parameter that can provide insight into an individual's health status. Image-based, walkways, wearable, and floor-vibrations technologies are the most current used technologies for estimating WS. In this paper, research from the last five years that explore each technology's capabilities on WS estimation and an evaluation of their technical and clinical aspects is presented.

Hsu-Nielsen source acoustic emission data on a concrete block.

Data presented in this paper are utilized in the paper entitled "Acoustic Emission Bayesian Source Location: Onset Time Challenge" [1]. Hsu-Nielsen source which also known as pencil lead break (PLB), is an artificial method of generating acoustic emission (AE) signals, which can roughly represent an acoustic emission damage source. The data in this paper represent AE signals emitted by conducting PLBs on a concrete block. The test was repeated ten times for three different locations. The resulted stress waves were captured by piezoelectric acoustic emission sensors and acquired as the electrical signals. The signals were digitized according to a specified sampling rate and were presented as voltage amplitudes. Each PLB was registered by several sensors (data acquisition channels). The data are presented for each PLB and channel. Furthermore, the geometry and mixture design of the concrete block, sensor types, sensor locations, and PLB locations are reported. The data can be used for validation of source location algorithms, signal processing, and sensor calibration.

A probabilistic approach for estimating water permeability in pressure-driven membranes.

A probabilistic approach is proposed to estimate water permeability in a cellulose triacetate (CTA) membrane. Water transport across the membrane is simulated in reverse osmosis mode by means of non-equilibrium molecular dynamics (MD) simulations. Different membrane configurations obtained by an annealing MD simulation are considered and simulation results are analyzed by using a hierarchical Bayesian model to obtain the permeability of the different membranes. The estimated membrane permeability is used to predict full-scale water flux by means of a process-level Monte Carlo simulation. Based on the results, the parameters of the model are observed to converge within 5-ns total simulation time. The results also indicate that the use of unique structural configurations in MD simulations is essential to capture realistic membrane properties at the molecular scale. Furthermore, the predicted full-scale water flux based on the estimated permeability is within the same order of magnitude of bench-scale experimental measurement of 1.72×10(-5) m/s.

Investigating the role of feedstock properties and process conditions on products formed during the hydrothermal carbonization of organics using regression techniques.

The purpose of this study is to develop regression models that describe the role of process conditions and feedstock chemical properties on carbonization product characteristics. Experimental data were collected and compiled from literature-reported carbonization studies and subsequently analyzed using two statistical approaches: multiple linear regression and regression trees. Results from these analyses indicate that both the multiple linear regression and regression tree models fit the product characteristics data well. The regression tree models provide valuable insight into parameter relationships. Relative weight analyses indicate that process conditions are more influential to the solid yields and liquid and gas-phase carbon contents, while feedstock properties are more influential on the hydrochar carbon content, energy content, and the normalized carbon content of the solid.