Accurate assessment of postural stability is crucial for monitoring patients with movement disorders, as it helps detect early signs of instability and prevent falls. Traditional methods, such as force platforms, are expensive, bulky, and limited to specialized laboratory settings, making them unsuitable for regular clinical screening or continuous home-based monitoring. In this work, we propose a deep learning approach to predict the Center of Pressure (CoP) trajectory using Inertial Measurement Unit (IMU) data from wearable smart glasses, offering a portable and cost-effective alternative. We use synchronized data from a force platform and a 9-axis IMU sensor to model the relationship between raw IMU signals and CoP force data (Force X and Y). The method involves windowing the IMU data, preprocessing it with low-pass filtering, and applying normalization. The dataset includes sequences from three standing tasks (eyes open, eyes closed, and free stance), captured at a frequency of 100 Hz. Experimental results show that the LSTM and BiLSTM models accurately predict CoP trajectories, achieving low Mean Absolute Error (MAE), Mean Squared Error (MSE), and high R2 values. While the TCN and GRU models face certain challenges in achieving the same level of performance as LSTM and BiLSTM, they present valuable insights and potential for future refinement. This approach has the potential to enable real-time, portable balance monitoring and early detection of postural instability, offering a scalable solution for clinical settings and home-based monitoring.

The use of multiple access protocols information in Internet of Things (IoT) environments has gained significant interest over the past decade, particularly for crowd behavior monitoring. Due to its high data rates and low infrastructure requirements, WiFi is considered one of the most promising wireless technologies for leveraging the explosive growth of transmitted data from mobile devices. However, with the introduction of MAC address randomization and the application of new randomization policies on assigning randomized sequence numbers (SNs) to transmitted frames in mobile devices to enhance privacy, traditional approaches to device identification face significant challenges. To tackle this issue, we conduct a comprehensive analysis at the multiple access level and propose CrowdWatch which is an innovative framework designed to enhance the understanding and utilization of MAC randomization dynamics. Additionally, we introduce a novel approach that leverages multiple device-specific features to accurately associate frames with randomized MAC addresses to their corresponding devices. By integrating multimodal fingerprints, the framework can effectively identify mobile devices and track their behavior. The presented approach ensures reliable detection even under the latest randomization policies. We examined the introduced framework through real-world experiments, and the findings prove that it is an effective solution for smart building management and occupancy estimation in dynamic environments.

Drones are integral to various applications, out of which traffic surveillance is an important application. However, their operational efficiency is limited by battery life, which restricts their capacity for extended critical missions. Additionally, in remote or high-interference areas, the bandwidth for drone communication is often limited, leading to a decrease in the quality of images transmitted to the base station. This paper aims to address such challenges by having drones transmit video data in real-time at lower resolutions for traffic monitoring. This approach conserves energy and optimizes transmission. However, it adversely affects object detection accuracy at the base station due to compromised data quality. To address this issue, we incorporate Generative Adversarial Networks (GANs) to improve LR images, restoring their quality for precise object detection. Results indicate that the accuracy of traffic analytics achieved with GAN-enhanced images is comparable to that obtained with high-resolution data transmission. Consequently, our approach allows a fundamental trade-off among drone energy consumption, transmission time, flight time, and object detection accuracy, enabling robust detection performance while conserving energy and enhancing operational capabilities.

Human balance is essential for everyday activities, from basic functions like standing and walking to more complex movements required in sports and work tasks. Optimal balance reduces the risk of falls, a major cause of injuries among the elderly and individuals with certain medical conditions. However, traditional force platforms can be prohibitively expensive for smaller clinics and individual practitioners, and patients with mobility issues may find it difficult to access locations equipped with such platforms. In light of these challenges, this work explores the effectiveness and versatility of using simpler hardware for pressure sensing. As an alternative to conventional force platforms, low-cost wearable sensors, such as Inertial Measurement Units (IMUs), have been explored. This research focuses on developing a simple yet effective balance evaluation system using smart-glasses embedded with an IMU sensor to replace traditional feet pressure sensing machines. Furthermore, we developed a custom dataset for the estimation of sway parameters by simultaneously collecting the IMU data and labels from the force platform for a set of 20 participants. We experimented with various Deep Learning (DL) models, leveraging the latest advancements in Machine Learning (ML), to estimate sway parameters such as sway path, sway area, and their ratio, typically measured by the gold standard force platform. When evaluated with appropriate performance criteria, the experimental results indicate that our proposed methodology performs robustly using only accelerometer data.

Alongside many traditional as well as novel applications, in the latest years, drones have been widely adopted as remote sensing platforms for road traffic monitoring in urban areas and on highways. The problem of traffic monitoring on Region of Interest (RoI) based on drone imagery is a challenging task, especially when the surveillance drone is constantly moving. In this work, two specific subtasks have been addressed. The goal of the first stage is to predict the RoI in drone imagery of traffic scenes using deep-learning (DL)-based approaches instead of traditional image processing; in this connection, the goal of the second task is to perform vehicle detection on the selected RoI. To ensure diversity and robustness, drone images with different altitudes, positions, and view points have been considered. To achieve these goals, two custom aerial data sets for RoI extraction and detection were built by collecting aerial sequences from flying unmanned aerial vehicles (UAVs) and by transmitting them to the base station leveraging 5G technology. Two different ad hoc DL-based architectures have been designed for the RoI extraction task to maximize the accuracy and inference speed, respectively, and have been evaluated on two different data sets: 1) a custom-built data set and 2) a Massachusetts roads data set. Finally, the models providing the best performance have been combined to further improve the overall results. Experimental tests show that the proposed framework represents a promising solution for drone-based road traffic monitoring in critical areas, exploiting imagery from a variety of viewing angles and altitudes.

Drones and video processing have become a vital and integrated aspect of smart city development in several applications such as search and rescue, surveillance and delivery. Newly, camera-equipped drones are flown, and high-resolution photos and video data are relayed via a communication link to the base station. Apart from tackling the video processing issues in applications, such as object identification and tracking, energy consumption that may present a stumbling block in completing a successful drone flight for data collecting must be highlighted and considered. Drones have a limited amount of energy storage, which must be used to power the drone's movement, hovering, data collection, and communication. This study aims at building and testing a drone energy profile that estimates the total energy consumed and a maximum flying time of a drone in a traffic monitoring scenario. Additionally, the relationship between the video processing task and the drone energy profile is explored to determine the optimal strategy for flying the drones while maintaining the video processing task's performance. The conclusion of this evaluation and investigation can be conceived of as the test-case scenario to fly a drone for surveillance and monitoring applications to attain the optimum results.

Unmanned aerial vehicles (UAVs) are increasingly utilized in smart cities to perform traffic monitoring tasks such as multiple object detection and tracking. The task's criticality is dependent on the drones' dynamic altitude, movable camera, and various viewing angles. These challenges are addressed once the UAVs' collected data is received. However, parameters affecting drone data collecting flight operations must also be explored, including drone actual flight time, data collection time, and energy consumption profile. The drone flight time would depend upon the battery capacity and energy consumption profile, which comprises drone movement, data collection, and communication energies. Besides, data collection energy consumption is subjected to video quality, frame rates, and data compression. The installed battery in UAVs is of limited capacity and determining actual flight time, data collection time, and energy consumption profile based on the factors mentioned above is critical. This paper develops and examines a drone energy consumption profile and proposes a drone flight strategy in a surveillance scenario to correctly estimate the drone's actual flight time, data collection time, and the distance the drone could travel from its original location. The results of this analysis are presented as a test case for flying a drone to collect the traffic monitoring data.

From smart cities development perspective, road vehicle detection exploiting drone-based aerial imagery is a crucial part of traffic surveillance and monitoring systems where effective results are of utmost demand. A recent boom in the field of deep learning (DL) has provided remarkable development in the problem of vehicle detection. Aerial views pose more complexity with respect to the ground view but the rapid advancement in the field of DL, the volume of data, and hardware configuration has facilitated the realization of these intelligent detection systems effectively. In this article, a detailed performance evaluation of some of the main state-of-the-art DL-based object detection techniques has been carried out along with an experimental analysis of vehicle detection using the RetinaNet framework on the VisDrone-benchmark data set. The performance of the RetinaNet framework has been validated together with the results provided by the VisDrone team. Further experiments are then conducted to investigate the impact of various parameters. Finally, the selection of suitable models that can be practically implemented is also discussed based both on a qualitative and quantitative analysis.