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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Cluster Sampling Method01:20

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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.
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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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A Machine Learning Approach for Estimating Person Counts Using Anonymous WiFi Data in a University Library.

Lucio Hernando-Cánovas1, Alejandro S Martínez-Sala1, Juan C Sánchez-Aarnoutse1

  • 1Department of Information and Communication Technologies, Universidad Politécnica de Cartagena (UPCT), 30202 Cartagena, Spain.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This study shows WiFi signals can accurately estimate indoor occupancy, offering a low-cost, privacy-preserving alternative to other technologies for building management.

Keywords:
WiFimachine learningoccupancysmart campuses

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Building Management Systems

Background:

  • Accurate indoor occupancy estimation is crucial for building management, energy optimization, and public health.
  • Existing technologies often lack accuracy, affordability, or privacy preservation.

Purpose of the Study:

  • To investigate the use of existing WiFi infrastructure as a non-intrusive sensing system for indoor occupancy estimation.
  • To develop and validate machine learning models for WiFi-based occupancy sensing.

Main Methods:

  • Utilized WiFi access points as soft sensors to collect anonymized connection metadata.
  • Trained supervised machine learning regression models (SVR, Ridge, MLP, XGBoost) on WiFi data.
  • Validated predictions against computer-vision ground truth in a university library over eight months.

Main Results:

  • Best-performing models (SVR, Ridge, MLP) achieved R² ≈ 0.95, with mean absolute errors of ~8 persons and SMAPE below 10% at medium-to-high occupancies.
  • XGBoost showed weaker generalization at extreme capacities due to data sparsity and hyperparameter sensitivity.
  • No temporal degradation was observed over the 8-month study, indicating long-term stability.

Conclusions:

  • WiFi-based occupancy estimation is a robust, cost-effective, and privacy-preserving solution.
  • This method offers a viable alternative for real-world building management applications.
  • The system demonstrates long-term stability and high accuracy in diverse occupancy scenarios.