## 4 Overview of Included Studies
The authors proposed Bindi, an end-to-end autonomous multimodal system designed to automatically detect violent situations by identifying fear-related emotions. The system follows a layered architecture consisting of the edge, fog, and cloud layers. The edge layer includes wearable devices (specifically a bracelet and a pendant) that collect physiological and auditory data, with the bracelet running a lightweight KNN algorithm for real-time fear detection. The fog layer is implemented on a smartphone device and performs the auditory analysis using a NN and multimodal data fusion to combine the physiological and auditory inputs. If a risky situation is detected an alarm is triggered. The cloud layer, built with MongoDB and NodeJS, stores encrypted data for long-term monitoring and evidence collection. This layered architecture ensures real-time processing, scalability, and secure handling of sensitive data. The system was tested using the open WEMAC dataset introduced by the authors, which includes physiological and auditory data from 47 women exposed to fear-inducing VR stimuli. The study evaluated three multimodal data fusion strategies, achieving an average fear recognition accuracy of 63.61%. The main limitations of this work are regarding the small population of the dataset as well as the fact that the physiological and speech data were not collected simultaneously, potentially reducing the accuracy of multimodal fusion. The accuracy of the model is low and balancing true and false positives remains a challenge. Computational constrains on the edge devices is another limitation restricting the use of more advanced algorithms such as NNs and necessitate the adoption of lightweight alternatives like KNN. Authors in [84] also proposed a novel intelligent wearable system for motion and emotion recognition that addresses limitations like single-function wearables and the lack of 3D DTs in similar systems. Their system effectively combines VR, AI and IoT for health monitoring and is composed of the hardware and the software system along with the 3D display platform. A helmet was designed which integrated a ten-axis accelerometer, gyroscope, camera, UWB chip for position tracking, a speaker with a recording function, and a TGAM module for electroencephalogram (EEG) signal acquisition. A Raspberry Pi serves as a processing terminal. The software system utilizes a DL network developed by the authors called the Three-Branch Spatial-Temporal Feature Extraction Network (TB-SFENet) for motion and emotion recognition. The TB-SFENet combines a bidirectional LSTM branch for temporal feature extraction, a transformer branch for global feature extraction, and a CNN branch for spatial feature extraction. The YOLOX algorithm was used for object recognition from the cameras' video feed. For emotion recognition, the authors created the TGAM Electroencephalogram Emotion Classification (TEEC) dataset and additionally used two well-known datasets for motion recognition with the system achieving 82.50%, 97.04% and 92.68% accuracy in each dataset respectively. Authors run other models such as MobileNet on the same datasets and demonstrated how TB-SFENet generally outperformed the rest. The previously mentioned 3D platform was built using UE5 and its purpose is to visualize the user's actions, emotions, and location in real-time, enabling interaction between the physical user and their DT. Feedback such as voice alerts for dangerous situations (e.g., falls or entering hazardous areas) is provided to the user after detection by the AI models. The system was furtherly tested in real scenarios,with motion recognition accurately synchronizing actions like standing, walking, and falling in the 3D DT platform and triggering voice alerts. Emotion recognition classified positive, negative, and calm states based on video-induced emotions, displaying them in the virtual environment. The system also demonstrated multi-person tracking capabilities, allowing simultaneous monitoring of multiple users.