Indoor localization (IL) is a significant topic of study with several practical applications, particularly in the context of the Internet of Things (IoT) and smart cities. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods within IoT environments. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, the survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of IoT-related industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

Digital Twin is the seamless data integration between a physical and virtual machine in either direction. Emotion recognition in healthcare is becoming increasingly important due to recent developments in Machine Learning methods. However, it may face technical problems such as limited datasets, occlusion and lighting issues, identifying key features, incorrect emotion classification, high implementation costs, head posture, and a person’s cultural background. This paper proposes a novel approach based on facial expression and body movement recognition for emotion recognition. It uses three devices (Kinect 1, Kinect 2, and RGB HD camera) to construct a new bi-modal database containing 17 participants’ performances of six emotional states. Two mono-modal classifiers have been developed to obtain sufficient state information based on facial expression and body motion analysis. The system’s performance is assessed using three algorithms: Bagged Trees, k -Nearest Neighbors ( k -NN), and Support Vector Machine (Linear and Cubic). The acquired findings demonstrate the excellent performance of the suggested method and the effectiveness of the proposed features, particularly the combination of 3D distance and 3D angle, in characterising and identifying emotions. Results obtained using Kinect 2 marginally surpass those with Kinect 1. Comparing 2D RGB and RGB-D data reveals that the depth information significantly raises the recognition rate. RGB-D features can be used to represent emotions, but there are discrepancies between RGB and RG-D data.

This paper proposes a hardware design of hand gesture recognition and its implementation on the Zynq platform (XC7Z020) of Xilinx. This proposed system is aimed to be embedded on the robotic prosthesis to improve the daily livings upper-limb amputees. Specifically, we design an architecture to identify hand movements using the Vivado HLS tool by exploiting the electromyography signal. The proposed architecture consists of creating two necessary Intellectual Property (IPs) on hardware designed, tested, and validated against the software implementation. The first one performs feature extraction from the EMG signal and the second one implements the classification using the k-nearest neighbour algorithm (k-NN). Our framework process EMG signals acquired using an MYO sensor with eight channels. The optimization of our design using pipeline directive achieves speed improvements of 5× and 2.15× for the feature extraction and Predict IPs, respectively, with moderate area resource consumption and the same performance as software implementation.

Indoor localization systems predominantly depend on one-dimensional signal measurements, such as the Received Signal Strength Indication (RSSI) from Bluetooth or WiFi access points (AP). Such methods, however, grapple with issues like interference from other APs and environmental challenges. To address these, our paper introduces an innovative indoor localization technique employing a classification system bolstered by transfer learning. Instead of relying solely on one-dimensional signals, we transform them into images using techniques like spectrograms, scalograms, or Gramian Angular Fields. These transformed images feed into our classification system using a transfer learning approach. We tested our method on two public datasets, achieving remarkable accuracy rates of 99% with the Google-Net model and 98% with the Squeeze-Net architecture. These figures underscore the efficacy of our technique for indoor localization, marking a notable advancement over existing strategies.

Augmented Reality (AR) technology offers promising applications in healthcare by enabling interactive 3D visualization of anatomical structures. However, current AR implementations often lack patient-specific detail, limiting their effectiveness in clinical settings. In this paper, we present BrainAR, an innovative mobile AR-based application designed for the automatic segmentation, 3D visualization, localization, and interaction with brain tumors using multiparametric 3D Magnetic Resonance Imaging (MRI) data. Our method leverages a 3D Residual U-Net, trained on the BraTS2021 dataset, achieving a mean Dice score of 0.886 for accurate tumor segmentation. The segmentation outputs are integrated into a real-time 3D engine to enable precise and dynamic visualization of brain tumors. Key contributions of our work include: 1) a server-side deployment of the segmentation model for online, patient-specific inference; 2) seamless AR integration enabling interactive exploration through hand gestures and voice commands; and 3) a mobile-based platform aimed at enhancing accessibility and usability in clinical environments. The proposed solution facilitates early detection and diagnosis by providing clinicians with an intuitive, immersive, and patient-specific tool for enhanced medical imaging interaction.