Identifying an Emotional State from Body Movements Using Genetic-Based Algorithms

• Published in: Artificial Intelligence and Soft Computing Vol. 10841; pp. 474 - 485
• Main Authors: Maret, Yann, Oberson, Daniel, Gavrilova, Marina
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Activity recognition; Biometric; Genetic algorithm; Image processing; Kinect sensor; Machine learning; Risk assessment
• ISBN: 3319912526; 9783319912523
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-91253-0_44

Emotions may not only be perceived by humans, but could also be identified and recognized by a machine. Emotion recognition through pattern analysis can be used in expert systems, lie detectors, medical emergencies, as well as during rescue operations to quickly identify people in distress. This paper describes a system capable of recognizing emotions based on the arm movement. Features extracted from 3D skeleton using Kinect sensor are classified by five commonly used machine learning techniques: K nearest neighbors, SVM, Decision tree, Neural Network and Naive Bayes. A genetic algorithm is then invoked to find the best system parameters to achieve the higher recognition rate. The system achieved 98.96% average accuracy on the experimental dataset.