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Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data.

Jorge Rodríguez1, Ari Y Barrera-Animas2, Luis A Trejo3

  • 1Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Carretera al Lago de Guadalupe Km. 3.5, Atizapán, Edo. de México C.P. 52926, Mexico. jorger@itesm.mx.

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PubMed
Summary
This summary is machine-generated.

A new algorithm, One-Class K-means with Randomly-projected features (OCKRA), enhances personal risk detection. OCKRA significantly improves detection performance using wearable sensor data, outperforming existing methods.

Keywords:
behavior analysisclassifier ensembleone-class classificationpersonal risk detectionwearable sensor

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Area of Science:

  • Machine Learning
  • Biomedical Engineering
  • Data Science

Background:

  • Ensembles of one-class classifiers are used in various applications.
  • Existing methods lack focus on personal risk detection.
  • Wearable sensors generate data for user monitoring.

Purpose of the Study:

  • Introduce the One-Class K-means with Randomly-projected features Algorithm (OCKRA).
  • Improve personal risk detection performance using the PRIDE dataset.
  • Evaluate OCKRA against established classification algorithms.

Main Methods:

  • OCKRA is an ensemble of one-class classifiers.
  • It utilizes multiple random feature subset projections.
  • The Personal RIsk DEtection (PRIDE) dataset, from 23 subjects using wearable sensors, was used for evaluation.

Main Results:

  • OCKRA outperformed Support Vector Machine and Parzen window classifiers.
  • Average Area Under the Curve (AUC) improvement was at least 0.53%.
  • OCKRA achieved over 90% AUC for more than 57% of users.

Conclusions:

  • OCKRA demonstrates superior performance in personal risk detection.
  • The algorithm shows promise for applications utilizing wearable sensor data.
  • OCKRA offers a significant advancement in detecting personal risk.