Summary of Sensing Technologies and Machine Learning Methods For Emotion Recognition in Autism: Systematic Review, by Oresti Banos et al.
Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review
by Oresti Banos, Zhoe Comas-González, Javier Medina, Aurora Polo-Rodríguez, David Gil, Jesús Peral, Sandra Amador, Claudia Villalonga
First submitted to arxiv on: 15 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study reviews the literature on Human Emotion Recognition (HER) systems in autism, exploring existing barriers and potential future directions. The authors conducted a systematic review of articles published between 2011 and 2023, focusing on sensing technologies and machine learning methods that involve children or adults with autism. The analysis identified key trends, including the primary use of facial expression techniques, video cameras as the most common device, and happiness, sadness, anger, fear, disgust, and surprise as the most frequently recognized emotions. Classical supervised machine learning techniques were dominant, whereas unsupervised approaches and deep learning models showed a growing trend. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well computers can understand people’s emotions, especially in people with autism. People with autism often have trouble reading and showing emotions like smiling or crying. This makes it hard to use regular computer systems that are designed for people without autism. The researchers looked through many studies on this topic to see what kinds of technology and techniques were used and what kind of emotions were recognized. They found that most studies used cameras to look at people’s faces, and the most common emotions they recognized were happiness, sadness, anger, fear, disgust, and surprise. The study also showed that more recent computer learning methods were starting to be used. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Supervised » Unsupervised
