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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning.

Zhanli Chen1, Rashid Ansari1, Diana J Wilkie2

  • 1Department of Electrical and Computer Engineering, University of Illinois at Chicago.

IEEE Transactions on Affective Computing
|March 4, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for detecting patient pain from facial expressions by analyzing combinations of facial muscle action units (AUs). The approach achieves high accuracy, offering a practical tool for pain monitoring in clinical settings.

Keywords:
Action Unit CombinationsAutomated Pain DetectionFACSFacial ExpressionMultiple Instance Learning

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Pain Medicine

Background:

  • Facial expressions, particularly combinations of action units (AUs) defined by the Facial Action Coding System (FACS), are reliable indicators of patient pain.
  • Existing Automated Facial Expression Recognition (AFER) systems struggle with pain detection, either by focusing on individual AUs or lacking sufficient data for direct pain classification.

Purpose of the Study:

  • To develop an automated system for detecting pain-related facial expression patterns, specifically focusing on AU combinations.
  • To improve the efficiency and practicality of pain monitoring through advanced computational methods.

Main Methods:

  • A two-tiered approach was developed, processing video frames for individual AU detection and then analyzing video sequences for AU combinations.
  • Novel data structures were created to encode AU combinations from single AU scores.
  • Weakly supervised learning frameworks, multiple instance learning (MIL) and multiple clustered instance learning (MCIL), were employed for pain recognition from video sequences.

Main Results:

  • The proposed system achieved 87% pain recognition accuracy and a 0.94 Area Under Curve (AUC) on the UNBC-McMaster Shoulder Pain Expression dataset.
  • Demonstrated potential for real-world application through successful testing on long videos from a lung cancer patient dataset.

Conclusions:

  • The developed method effectively detects pain from facial expressions by analyzing AU combinations, outperforming existing AFER systems for this specific task.
  • The system shows significant promise for practical pain monitoring in clinical environments, aiding healthcare professionals in pain assessment.