This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License. (http://creativecommons.org/licenses/by-nd/4.0)If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium. Purpose: The aim of this study was to explore characteristics of and risk factors for accidental inpatient falls. Methods: Participants were classified as fallers or non-fallers based on the fall history of inpatients in a tertiary hospital in Seoul between June 2014 and May 2015. Data on falls were obtained from the fall report forms and data on risk factors were obtained from the electronic nursing records. Characteristics of fallers and non-fallers were analyzed using descriptive statistics. Risk factors for falls were identified using univariate analyses and logistic regression analysis. Results: Average length of stay prior to the fall was 21.52 days and average age of fallers was 61.37 years. Most falls occurred during the night shifts and in the bedroom and were due to sudden leg weakness during ambulation. It was found that gender, BMI, physical problems such elimination, gait, vision and hearing and medications such as sleeping pills, antiarrhythmics, vasodilators, and muscle relaxant were statistically significant factors affecting falls. Conclusion: The findings show that there are significant risk factors such as BMI and history of surgery which are not part of fall assessment tools. There are also items on fall assessment tools which are not found to be significant such as mental status, emotional unstability, dizziness, and impairment of urination. Therefore, these various risk factors should be examined in the fall risk assessments and these risk factors should be considered in the development of fall assessment tools.

In this paper, we propose a parametric sparse recovery algorithm(SRA) applied to a radar signal model, based on the compressive sensing(CS), for the ISAR(Inverse Synthetic Aperture Radar) image reconstruction from an incomplete radar-cross-section(RCS) data and for the estimation of rotation rate of a target. As the SRA, the iteratively-reweighted-least-square(IRLS) is combined with the radar signal model including chirp components with unknown chirp rate in the cross-range direction. In addition, the particle swarm optimization(PSO) technique is considered for searching correct parameters related to the rotation rate. Therefore, the parametric SRA based on the IRLS can reconstruct ISAR image and estimate the rotation rate of a target efficiently, although there exists missing data in observed RCS data samples. The performance of the proposed method in terms of image entropy is also compared with that of the traditional interpolation methods for the incomplete RCS data.

It takes considerable time to generate accurate clutter signal using conventional clutter generation scheme. In this paper, real-time schemes are proposed, which have reasonable accuracy and are applicable to testing the radar performance. Proposed methods are compared through the simulation, which represented that clutter signal can be generated in real-time when using proposed methods for simulated signal generator.