## 2 Mini Survey
### Recent Trends and Developments of ADL Studies
To associate emotions and behavior patterns recognized from images, benchmark datasets were provided: interactive emotional dyadic motion capture (IEMO-CAP) datasets [179], the multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos (MOSI) [180], one-minute-gradual (OME) emotion datasets, emotion recognition in the wild named EmotiW [181], and the affect-net database [182]. For these datasets, the associated emotions comprise disgust, fear, happiness, surprise, sadness, and anger defined by [PERSON] [183]. Combined with these six basic emotion categories, [PERSON] et al. [184] added nine complex emotion categories: curiosity, uncertainty, excitement, attentiveness, exploration, confusion, anxiety, embarrassment, and frustration. Moreover, [PERSON] et al. [185] added 11 complex emotional categories: amusement, contempt, contentment, embarrassment, excitement, guilt, pleasure, pride, relief, satisfaction, and shame. As a smart classroom application at an elementary school, [PERSON] et al. [186] proposed a system that includes affective sensing, DL-based emotion recognition, and real-time mobile-cloud computing networks. Their system yielded real-time suggestions with an in-class presenter to improve the quality and memorability of their allowed presentation. Moreover, their system yielded real-time adjustments and corrections to their non-verbal behavior, such as hand gestures, facial expressions, and body language. Their comprehensive study found computational requirements for their proposed system, which incorporates these technologies. Based on these requirements, they assess current difficulties and suggest some future directions in engineering and education disciplines for system deployment. They merely designed a conceptual system and its framework without specific classification or recognition of motion or behavior patterns. Using a depth camera for three-dimensional (3D) human pose reconstruction, [PERSON] et al. [187] introduced a challenging task to recognize emotions from fine-grained actions. They developed non-staged videos recorded during robot-assisted therapy sessions for children with autism, along with non-standard camera viewpoints. Based on this development, they presented challenges: a large dataset with long videos, numerous highly variable actions, children who are only partially visible, and children of different ages who might show unpredictable actions. They investigated 3D human pose reconstruction methods for newly introduced tasks. Furthermore, they proposed extensions to adapt them to address these challenges. Moreover, for establishing several baselines and implications in the context of a child-robot interaction, they analyzed multiple approaches to action and emotion recognition from 3D human pose features. For extraction of emotion-specific features from a vast number of human body motion descriptors, [PERSON] et al. [188] introduced a double layer feature selection framework to classify emotions from a comprehensive list that included body motion features of 10 types. They used that feature selection framework to achieve accurate recognition of five basic emotions of happiness, sadness, fear, anger, and neutral, combined with three scenarios of walking, sitting, and action-independent scenarios. They validated their method using open datasets [189] obtained using a depth camera from 30 subjects. The experimentally obtained results revealed that their proposed emotion recognition system achieved excellent emotion recognition. In fact, it outperformed five widely used CML methods: SVM, linear discriminant analysis (LDA), naive Bayes (NB) classifier, \(k\)NN, and decision tree (DT). Nevertheless, its recognition accuracy was strongly dependent on the scenarios which were set in advance.