Child Facial Video Analysis for Automated Pain Detection, Aided by Human-Guided Transfer Learning
In a groundbreaking study, researchers have developed a new approach to recognise pain in children using computer vision algorithms and Facial Action Units (AUs) defined by the Facial Action Coding System (FACS). The study, which focused on accurately determining pain levels in children, applied a transfer learning method to improve classification performance in pain recognition.
The transfer learning method was used to map automated AU codings to a subspace of manual AU codings, enabling more robust pain recognition performance when only automatically coded AUs are available for the test data. This approach leverages knowledge learned from related facial analysis tasks or larger datasets to improve feature extraction and classification accuracy on limited pediatric pain data.
By initialising models with pre-trained weights from broadly trained facial recognition or emotion detection networks, the transfer learning method allows computer vision algorithms to better detect and interpret subtle pain-related facial actions in children. Fine-tuning these models on pediatric pain datasets further improves the identification of the presence and intensity of pain from facial cues despite limited labelled child-specific data.
The study found that classifiers based on manually coded AUs demonstrated reduced environmentally-based variability in performance compared to those based on automated AU codings. This suggests that a combination of automated and manual AU codings may be beneficial for pain recognition.
While the study did not explicitly discuss transfer learning applied directly to pediatric pain recognition via AUs, related advances in improved transfer-learning frameworks have been demonstrated in detecting autism spectrum disorder from children's faces. This underscores the effectiveness of the transfer learning approach in similar pediatric facial analysis tasks.
The study's findings indicate that the transfer learning method can potentially improve the performance of pain recognition systems that use only automatically coded AUs. With the transfer learning method, the Area under the ROC Curve (AUC) on independent data was improved from 0.69 to 0.72.
Facial activity continues to provide sensitive and specific information about pain. The study involved trained professionals and parents, and the results underscore the importance of considering both automated and manual AU codings in pain recognition research.
In conclusion, the transfer learning method enhances pain recognition performance in children using automated AU coding and computer vision algorithms. This approach optimises automated facial coding of pain, improving clinical pain assessment and monitoring in pediatric populations using computer vision algorithms powered by transfer learning.
- Technology, such as artificial intelligence, and computer vision algorithms are now being used in science, particularly in the field of health and wellness, to aid in the accurate recognition of chronic diseases like pain, even in children.
- The revolution in pain recognition methodologies includes using subspaces of manual and automated Facial Action Unit (AU) codings, a process facilitated by technology like artificial intelligence, which improves the classification performance in pain recognition.
- Mental health, including conditions that affect children, may also benefit from advancements in technology, such as artificial intelligence, as transfer learning frameworks have proven effective in detecting conditions like autism spectrum disorder.
- In the realm of medical-conditions, especially those that manifest in a subtle manner like chronic pain in children, technology like artificial intelligence, powered by transfer learning, can help optimize automated facial coding, rendering it more robust and accurate.
