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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Related Experiment Video

Updated: Aug 19, 2025

Design and Analysis for Fall Detection System Simplification
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Design and Analysis for Fall Detection System Simplification

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Development of an effective clustering algorithm for older fallers.

Choon-Hian Goh1,2, Kam Kang Wong1, Maw Pin Tan3,4

  • 1Department of Mechatronics and BioMedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia.

Plos One
|November 28, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a clustering algorithm to identify fall risks in older adults. The algorithm effectively groups individuals into low, intermediate, and high fall risk categories, aiding prevention efforts.

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Data Science

Background:

  • Falls are a significant health concern for older adults, leading to severe physical and psychological outcomes.
  • Assessing fall risk is complex due to multiple interacting factors and is often time-consuming in clinical settings.

Purpose of the Study:

  • To develop and validate a clustering-based algorithm for efficient and accurate determination of fall risk in older individuals.
  • To create a tool that can improve access to falls prevention strategies.

Main Methods:

  • Utilized data from the Malaysian Elders Longitudinal Research (MELoR) dataset (n=1411, age ≥55).
  • Employed data pre-processing, feature extraction (t-Distributed Stochastic Neighbour Embedding or principal component analysis), and clustering (K-means, Hierarchical, Fuzzy C-means).
  • Interpreted cluster characteristics using statistical analysis on 1279 subjects and 9 variables.

Main Results:

  • The t-SNE and K-means algorithm successfully clustered subjects into four groups: low (13% falls), intermediate A (19% falls), intermediate B (21% falls), and high (31% falls) risk.
  • Key identified risk factors included slower gait, poorer balance, weaker muscle strength, cardiovascular disorders, cognitive decline, and advanced age.
  • The algorithm effectively grouped individuals based on identified risk features.

Conclusions:

  • A novel clustering algorithm can effectively stratify older adults into distinct fall risk categories.
  • This tool has the potential to serve as a valuable clinical decision support system for early case identification and targeted falls prevention.
  • Enhancing accessibility to falls prevention programs is crucial for mitigating the impact of falls in the elderly population.