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Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and

Jithin S Sunny1, C Pawan K Patro2, Khushi Karnani1

  • 1Rhenix Lifesciences, Hyderabad 500038, India.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary

Wearable devices collect vital health data. Automated anomaly detection in this data is crucial for understanding disease and improving personal healthcare, using various machine learning techniques.

Keywords:
anomaly detectionheart ratemachine learningmissing datawearables

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Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Data Science

Background:

  • Wearable devices continuously monitor physiological parameters like heart rate and activity levels.
  • This data offers potential for personalized healthcare assessment and disease pattern identification.
  • Anomalies in physiological data can indicate underlying health issues.

Purpose of the Study:

  • To review data processing methods for anomaly detection in wearable device sensor data.
  • To explore supervised, unsupervised, and semi-supervised techniques for anomaly identification.
  • To address challenges posed by missing and un-annotated healthcare data.

Main Methods:

  • Review of existing literature on wearable device data and anomaly detection.
  • Analysis of data processing techniques for physiological parameters.
  • Evaluation of machine learning approaches (supervised, unsupervised, semi-supervised).

Main Results:

  • Anomalies in wearable data are clinically significant for diagnosis and treatment.
  • Accurate automated techniques are necessary for identifying these anomalies.
  • Various detection methods have been proposed, with ongoing research in machine learning.

Conclusions:

  • Wearable devices generate vast amounts of data requiring sophisticated anomaly detection.
  • Machine learning techniques are essential for accurate identification of health-related anomalies.
  • Addressing data limitations is key to leveraging wearable technology for healthcare.