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Cluster Sampling Method

Cluster Sampling Method

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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Unsupervised Human Activity Recognition Using the Clustering Approach: A Review.

Paola Ariza Colpas¹, Enrico Vicario², Emiro De-La-Hoz-Franco¹

¹Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia.

Sensors (Basel, Switzerland)

|May 14, 2020

Summary

The Internet of Things (IoT) in healthcare enables better patient management through data analysis. This review focuses on clustering techniques for analyzing daily living activities, aiding in patient care and treatment improvement.

Keywords:

activities of daily living–ADL activity recognition systems–ARS ambient assisted living–AAL clustering human activity recognition–HAR unsupervised activity recognition

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

Computer Science
Health Informatics
Data Science

Background:

The Internet of Things (IoT) has numerous applications, particularly in healthcare, aiming to enhance patient quality of life and home treatment.
Analyzing daily living activities is crucial for managing patients with various pathologies.

Purpose of the Study:

To conduct a systematic literature review on the application of Clustering methods for analyzing daily living activities.
To identify key variables such as publication year, article type, algorithms, datasets, and metrics used in this field.

Main Methods:

Systematic literature review.
Analysis of unsupervised data using Clustering techniques.
Identification and description of relevant variables.

Main Results:

Clustering is a widely used unsupervised data analysis technique for daily living activities.
The review details common algorithms, datasets, and evaluation metrics.
Publication trends and article types are analyzed.

Conclusions:

This review provides an overview of recent advancements in applying Clustering to daily living activity analysis in healthcare.
The findings help researchers and practitioners understand the current landscape and identify future research directions.