textBackground: Fall-related injuries, hospitalizations, and mortality among older persons represent a major public health problem. Owing to aging societies worldwide, a major impact on fall-related health care demand can be expected. We determined time trends in numbers and incidence of fall-related hospital admissions and in admission duration in older adults. Methods: Secular trend analysis of fall-related hospital admissions in the older Dutch population from 1981 through 2008, using the National Hospital Discharge Registry. All fall-related hospital admissions in persons 65 years or older were extracted from this database. Outcome measures were the numbers, and the age-specific and age-adjusted incidence rates (per 10 000 persons) of fall-related hospital admissions in each year of the study. Results: From 1981 through 2008, fall-related hospital admissions increased by 137%. The annual age-adjusted incidence growth was 1.3% for men vs 0.7% for women (P 75 years). Although the incidence of fall-related hospital admissions increased, the total number of fall-related hospital days was reduced by 20% owing to a reduction in admission duration. Conclusions: In the Netherlands, numbers of fall-related hospital admissions among older persons increased drastically from 1981 through 2008. The increasing fall-related health care demand has been compensated for by a reduced admission duration. These figures demonstrate the need for implementation of falls prevention programs to control for increases of fall-related health care consumption.

https://repub.eur.nl/pub/25670/10_Hartholt et al_Arch Intern Med_2010_17010_905-911AAM.pdf

Trends in fall-related hospital admissions in older persons in the Netherlands

Input-state-parameter estimation of structural systems from limited output information

Many physics-based and surrogate models used in structural health monitoring are affected by different sources of uncertainty such as model approximations and simplified assumptions. Optimal struct...

/pdf/a-variational-bayesian-neural-network-for-structural-health-1wab0hw84r.pdf

A variational Bayesian neural network for structural health monitoring and cost-informed decision-making in miter gates:

Developing optimum solutions to engineering problems typically relies on structured and complex thought processes that require evaluation, interpretation and opinion. Well-developed critica...

A literature review of critical thinking in engineering education

A novel feature representation method based on original waveforms for acoustic emission signals

Acoustic emission Bayesian source location: Onset time challenge

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

Proposes a Benchmark problem for vibration-based human activity monitoring.Describes a floor vibrations dataset to design algorithms for human monitoring.Benchmark is in seven cases in increasing difficulty.Standard metrics based on the available experimental data are proposed.An example to identify different human activity from floor vibrations is discussed. Monitoring and analyzing floor vibrations to determine human activity has major applications in fields such as health care and security. For example, structural vibrations could be used to determine if an elderly person living independently falls, or if a room is occupied or empty. Monitoring human activity using floor vibration promises to have advantages over other methods. For example, it does not have the privacy concerns of other methods such as vision-based techniques, or the compliance challenges of wearable sensors. The analysis of the signals becomes a classification problem determining the type of human activity. Unfortunately only a few research groups are performing research of this subject even though there is a significant number of techniques that could be applied to this field. To date, no systematic study about the challenges and advantages of using different types of algorithms for this problem has been performed. This paper proposes a benchmark problem to: (i) encourage researchers to design new algorithms for monitoring human activity using floor vibrations, (ii) provide a dataset to test new algorithms, and (iii) allow the comparison of proposed methods based on a set of standard metrics. The benchmark consists of seven different cases of increasing difficulty. Each case has a specific number of sensors, calibration signals, and type of floor excitation forces to be considered. The paper also proposes specific metrics that enable the direct comparison of different techniques. Research groups interested in monitoring human activity using floor vibrations are encouraged to use the experimental data and evaluation metrics published in this paper to develop their own methodologies. This will enable the community of researchers to easily compare and contrasts techniques and better understand what type of methods will be appropriate in different applications.

Benchmark problem for human activity identification using floor vibrations

Bayesian updating and identifiability assessment of nonlinear finite element models

One of the major challenges in the implementation sampling techniques in the Bayesian approaches is the computational cost involved with the estimation of the likelihood and/or posterior, especially in problems where the models being updated are computationally expensive. This paper proposes the use of surrogate models in a two-layer Bayesian approach to reduce the computational cost of estimating these PDF. In the first layer, the posterior is written in a traditional manner. The second layer attempts to estimate the PDF of the first layer with a surrogate model. Only a few runs of the structural model are needed to create the required samples. Preliminary results are shown with a numerical example to identify the stiffness of a structural system. Only ten simulations of the structural model are used to estimate the posterior PDF.

Ramin Madarshahian

Papers

Acoustic emission Bayesian source location: Onset time challenge

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

Benchmark problem for human activity identification using floor vibrations

Bayesian updating and identifiability assessment of nonlinear finite element models

Reducing MCMC Computational Cost with a Two Layered Bayesian Approach