## 2 Activity Recognition-Related Works
### Typically Developmental People  
The multi-modal architecture in [10] uses sequences of 3D skeletons from a Kinect tool. Three parts of the architecture are developed by [PERSON] et al. A dual-stream C3D is used in the first two channels to extract space-time information from 3D depth data. The 3D skeleton sequence is incorporated into the third channel to improve the accuracy of 3D behavior detection. Recognition is enhanced through the fusion of Support Vector Machine (SVM) classification results from C3D characteristics with the human skeleton. Scale and orientation invariance are guaranteed by the skeleton representation, and experimental findings show good discriminatory power even for comparable activities. In [PERSON] et al. [11], the authors provide a unique framework for detecting spatiotemporal activities in untrimmed videos to effectively identify and classify numerous contemporaneous activities. It incorporates appearance and motion cues using deep learning architectures such as Region Proposal Networks (RPNs) and Fast R-CNNs. The approach uses two-pass dynamic programming optimization to create \"action tubes\" (temporal sequences of observed areas). Red-Green-Blue (RGB) and optical flow data provide cutting-edge performance on datasets such as UCF101 and J-HMDB-21, considerably enhancing detection accuracy and speed. This method is ideal for complicated scenarios with overlapping activities and untrimmed video settings. Moreover, [PERSON] et al. [12] introduce a framework for online, real-time spatiotemporal activity localization and prediction in videos. Traditional approaches for activity detection are often offline and require complete access to the entire video, rendering them unsuitable for real-time applications. To solve this issue, the authors propose a system based on the Single Shot MultiBox Detector (SSD) for frame-level activity detection, paired with a greedy, incremental technique for generating and labeling \"activity tubes\" (temporal sequences of observed activity areas). This system enables early activity prediction and efficiently analyzes streaming video, delivering cutting-edge performance on benchmarks such as UCF101-24 and J-HMDB 21. Surveillance, human-robot interaction, and autonomous systems are all examples of applications that need the quick and precise identification of human activities. In the context of finding more advanced deep learning approaches to handle the activity recognition problem, we find that Deep Reinforcement Learning (DRL) is one of the most promising methods in this domain. In [13], the authors propose a Deep Progressive Reinforcement Learning (DPRL) approach for skeleton-based activity recognition, aiming to identify key frames in skeletal video sequences. By treating frame selection as a MarkovDecision Process, the model employs a reinforcement learning agent to progressively distill the most informative frames, reducing noise from irrelevant or ambiguous ones. For activity recognition, the approach leverages a Graph Convolutional Neural Network (GCNN) to exploit the intrinsic and extrinsic dependencies among human joints, structured as graph vertices and edges. This dual methodology enhances temporal and spatial feature learning, achieving competitive results across benchmark datasets, including NTU+RGBD, SYSU-3D, and UT-Kinect, with applications in surveillance and human-computer interaction. Another study adopts DRL to optimize activity detection steps in untrimmed videos instead of using extensive proposal generation or an expensive sliding window [14]. [PERSON] et al. [14] propose using the Markov Decision Process (MDP) to detect activities. An agent modifies its location and scale via seven transformations within a temporal window to cover actual activity zones. Through training, the agent learns the best policy based on each action's negative and positive rewards. This policy directs the search while testing. Deep Q-Network (DQN) learns the best agent policy for producing accurate activity proposals.