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Wearable-Based Affect Recognition-A Review.

Philip Schmidt1,2, Attila Reiss3, Robert Dürichen4

  • 1Robert Bosch GmbH, Robert-Bosch-Campus 1, 71272 Renningen, Germany. philip.schmidt@de.bosch.com.

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Summary
This summary is machine-generated.

This paper reviews wearable affect recognition, focusing on physiological sensors for long-term stress monitoring. It offers a comprehensive guide for researchers developing and evaluating wearable systems for emotion detection.

Keywords:
affect recognitionaffective computingdata collectionmachine learningphysiological featurephysiological signalsreviewsensorswearables

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Area of Science:

  • Interdisciplinary research combining natural and social sciences.
  • Focus on affect recognition for decision-making support and mental wellbeing.
  • Exploration of wearable sensors for real-time physiological and inertial data collection.

Background:

  • Affect recognition aims to identify emotional states from observable data.
  • Wearable sensors offer a platform for continuous, in-home affect monitoring.
  • Existing literature lacks a comprehensive overview of wearable-based affect recognition.

Purpose of the Study:

  • To provide a broad overview of wearable affect and stress recognition.
  • To detail theoretical background, methods, and best practices.
  • To enable researchers to conduct user studies and develop wearable systems.

Main Methods:

  • Review of psychological models of affect.
  • Analysis of physiological changes associated with affective states.
  • Examination of sensors for physiological measurement.
  • Outline of lab protocols and ground truth generation for field studies.
  • Description of data processing, feature extraction, and classification techniques.

Main Results:

  • Identification of key physiological parameters and sensors for affect recognition.
  • Guidelines for eliciting affective states and collecting ground truth data.
  • Overview of standard data processing pipelines for wearable sensor data.
  • Review of common machine learning approaches for affect classification.

Conclusions:

  • Wearable systems are crucial for long-term affect recognition.
  • A comprehensive understanding of methods and best practices is needed.
  • This review serves as a guide for future research and development in wearable affect recognition systems.