Search Results (3,800)

Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy. Here, we present vGait, a comprehensive 3D gait assessment method using a single RGB-D sensor and state-of-the-art pose-tracking algorithms. vGait was validated in healthy participants during frontal- and sagittal-perspective walking. Performance was comparable across perspectives, with vGait achieving high accuracy in detecting initial and final foot contacts (F1 scores > 95%) and reliably quantifying spatiotemporal gait parameters (e.g., stride time, stride length) and whole-body coordination metrics (e.g., arm swing and knee angle ROM) at different levels of granularity (mean, step-to-step variability, side asymmetry). The flexibility, accuracy, and minimal resource requirements of vGait make it a valuable tool for clinical and non-clinical applications, including outpatient clinics, medical practices, nursing homes, and community settings. By enabling efficient and scalable gait assessment, vGait has the potential to enhance diagnostic and therapeutic workflows and improve access to clinical mobility monitoring. Full article

The digital transition is one of the biggest challenges of the new millennium. One of the key drivers of this transition is the need to adapt to the rapidly changing and heterogeneous technological landscape that is continuously evolving. Digital Twin (DT) technology can promote this transition at an urban scale due to its ability to monitor, control, and predict the behaviour of complex systems and processes. As several scientific studies have shown, DTs can be developed for infrastructure and city management, facing the challenges of global changes. DTs are based on sensor-distributed networks and can support urban management and propose intervention strategies based on future forecasts. In the present work, a three-axial operative framework is proposed for developing a DT urban management system using the city of Venice as a case study. The three axes were chosen based on sustainable urban development: energy, mobility, and resiliency. Venice is a fragile city due to its cultural heritage, which needs specific protection strategies. The methodology proposed starts from the analysis of the state-of-the-arts of DT technologies and the definition of key features. Three different axes are proposed, aggregating the key features in a list of fields of intervention for each axis. The Venice open-source database is then analysed to consider the data already available for the city. Finally, a list of DT services for urban management is proposed for each axis. The results show a need to improve the city management system by adopting DT. Full article

The determination of diameter at breast height (DBH) is critical in forestry, serving as a key metric for deriving various parameters, including tree volume. Light Detection and Ranging (LiDAR) technology has been increasingly employed in forest inventories, and the development of cost-effective, user-friendly smartphone and tablet applications (apps) has expanded its broader use. Among these are augmented reality (AR) apps, which have already been tested on mobile devices for their accuracy in measuring forest attributes. In February 2024, Apple introduced the Mixed-Reality Interface (MRITF) via the Apple Vision Pro (AVP), offering sensor capabilities for field data collection. In this study, two apps using the AVP were tested for DBH measurement on 182 trees across 22 sample plots in a near-natural forest, against caliper-based reference measurements. Compared with the reference measurements, both apps exhibited a slight underestimation bias of −1.00 cm and −1.07 cm, and the root-mean-square error (RMSE) was 3.14 cm and 2.34 cm, respectively. The coefficient of determination (R²) between the reference data and the measurements obtained by the two apps was 0.959 and 0.978. The AVP demonstrated its potential as a reliable field tool for DBH measurement, performing consistently across varying terrain. Full article

Traditional methods for evaluating tennis technique, such as visual observation and video analysis, are often subjective and time consuming. On the other hand, a quick and accurate assessment can provide immediate feedback to players and contribute to technical development, particularly in less experienced athletes. This study aims to validate the use of a single inertial measurement system to assess some relevant technical parameters of amateur players. Among other things, we attempt to search for significant correlations between the flexion extension and torsion of the torso and the lateral distance of the ball from the body at the instant of impact. This research involved a group of amateur players who performed a series of standardized gestures (forehands and serves) wearing a sensorized chest strap fitted with a wireless inertial unit. The collected data were processed to extract performance metrics. The percentage coefficient of variation for repeated measurements, Wilcoxon signed-rank test, and Spearman’s correlation were used to determine the system’s reliability. High reliability was found between sets of measurements in all of the investigated parameters. The statistical analysis showed moderate and strong correlations, suggesting possible applications in assessing and optimizing specific aspects of the technique, like the player’s distance to the ball in the forehand or the toss in the serve. The significant variations in technical execution among the subjects emphasized the need for tailored interventions through personalized feedback. Furthermore, the system allows for the highlighting of specific areas where intervention can be achieved in order to improve gesture execution. These results prompt us to consider this system’s effectiveness in developing an on-court mobile application. Full article

The Internet of Vehicles (IoV), a key component of smart transportation systems, leverages 5G communication for low-latency data transmission, facilitating real-time interactions between vehicles, roadside units (RSUs), and sensor networks. However, the open nature of 5G communication channels exposes IoV systems to significant security threats, such as eavesdropping, replay attacks, and message tampering. To address these challenges, this paper proposes the Efficient Cluster-based Mutual Authentication and Key Update Protocol (ECAUP) designed to secure IoV systems within 5G-enabled sensor networks. The ECAUP meets the unique mobility and security demands of IoV by enabling fine-grained access control and dynamic key updates for RSUs through a factorial tree structure, ensuring both forward and backward secrecy. Additionally, physical unclonable functions (PUFs) are utilized to provide end-to-end authentication and physical layer security, further enhancing the system’s resilience against sophisticated cyber-attacks. The security of the ECAUP is formally verified using BAN Logic and ProVerif, and a comparative analysis demonstrates its superiority in terms of overhead efficiency (more than 50%) and security features over existing protocols. This work contributes to the development of secure, resilient, and efficient intelligent transportation systems, ensuring robust communication and protection in sensor-based IoV environments. Full article

A typical Wireless Sensor Network (WSN) defines the usage of static sensors; however, the growing focus on smart cities has led to a rise in the adoption of mobile sensors to meet the varied demands of Internet of Things (IoT) applications. This results in significantly increasing dependencies towards secure storage and effective resource management. One way to address this issue is to harness the immutability property of the Ethereum blockchain. However, the existing challenges in IoT communication using blockchain are noted to eventually lead to symmetry issues in the network dynamics of Ethereum. The key issues related to this symmetry are scalability, resource disparities, and centralization risk, which offer sub-optimal opportunities for nodes to gain benefits, influence, or participate in the processes in the blockchain network. Therefore, this paper presents a novel blockchain-based computation model for optimizing resource utilization and offering secure data exchange during active communication among mobile sensors. An empirical method of trust computation was carried out to identify the degree of legitimacy of mobile sensor participation in the network. Finally, a novel cost model has been presented for cost estimation and to enhance the users’ quality of experience. With the aid of a simulation study, the benchmarked outcome of the study exhibited that the proposed scheme achieved a 40% reduced validation time, 28% reduced latency, 23% improved throughput, 38% minimized overhead, 27% reduced cost, and 38% reduced processing time, in contrast to the existing blockchain-based solutions reported in the literature. This outcome prominently exhibits fairer symmetry in the network dynamics of Ethereum presented in the proposed system. Full article

Here we describe the synthesis and evaluation of a molecular corrosion sensor that can be applied in situ in aerospace coatings, then used to detect corrosion after the coating has been applied. A pH-sensitive molecule, 4-mercaptopyridin (4-MP), is attached to a gold nanoparticle to allow surface-enhanced Raman-scattering (SERS) for signal amplification. These SERS nanoparticles, when combined with an appropriate micron-sized carrier system, are incorporated directly into an MIL-SPEC coating and used to monitor the process onset and progression of corrosion using pH changes occurring at the metal–coating interface. The sensor can track corrosion spatially as it proceeds underneath the coating, due to the mobility of the proton front generated during corrosion and the homogeneous distribution of the sensor in the coating layer. To our knowledge, this report is the first time a 4-MP functionalized gold nanoparticle has been used, along with SERS spectroscopy, to monitor corrosion in an applied commercial coating in a fast, non-contact way. Full article

In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively. Full article

A novel path-planning method utilizing the trapezoid, adjacent, and distance, (TAD) characteristics of frontiers is presented in this work. The method uses the mobile robot’s sensor range to detect frontiers throughout each exploration cycle, modifying them at regular intervals to produce their parameters. This well-thought-out approach makes it possible to choose objective points carefully, guaranteeing seamless navigation. The effectiveness and applicability of the suggested approach with respect to exploration time and distance are demonstrated by empirical validation. Results from experiments show notable gains over earlier algorithms: time consumption decreases by 10% to 89% and overall path distance for full investigation decreases by 12% to 74%. These remarkable results demonstrate the efficacy of the suggested approach and represent a paradigm change in improving mobile robot exploration in uncharted territory. This research introduces a refined algorithm and paves the way for greater efficiency in autonomous robotic exploration. This study opens the door for more effective autonomous robotic exploration by introducing an improved algorithm. Full article

The deployment of Advanced Air Mobility requires the continued development of technologies to ensure operational safety. One of the key aspects to consider here is the availability of robust solutions to avoid tactical conflicts between drones and other flying elements, such as other drones or birds. Bird detection is a relatively underexplored area, but due to the large number of birds, their shared airspace with drones, and the fact that they are non-cooperative elements within an air traffic management system, it is of interest to study how their detection can be improved and how collisions with them can be avoided. This work demonstrates how a LiDAR sensor mounted on a drone can detect birds of various sizes. A LiDAR simulator, previously developed by the Aerolab research group, is employed in this study. Six different collision trajectories and three different bird sizes (pigeon, falcon, and seagull) are tested. The results show that the LiDAR can detect any of these birds at about 30 m; bird detection improves when the bird gets closer and has a larger size. The detection accuracy is higher than 1 m in most of the cases under study. The errors grow with increasing drone-bird relative speed. Full article

Background: There has been a growing interest in using inertial sensors to explore the temporal aspects of the Timed Up and Go (TUG) test. The current study aimed to analyze the spatiotemporal parameters and phases of the TUG test in patients with knee osteoarthritis (KOA) and compare the results with those of non-arthritic individuals. Methods: This study included 20 patients with KOA and 60 non-arthritic individuals aged 65 to 84 years. All participants performed the TUG test, and 17 spatiotemporal parameters and phase data were collected wirelessly using the BTS G-Walk inertial sensor. Results: Significant mobility impairments were observed in KOA patients, including slower gait speed, impaired sit-to-stand transitions, and reduced turning efficiency. These findings highlight functional deficits in individuals with KOA compared to their non-arthritic counterparts. Conclusions: The results emphasize the need for targeted physiotherapy interventions, such as quadriceps strengthening, balance training, and gait retraining, to address these deficits. However, the study is limited by its small sample size, gender imbalance, and limited validation of the BTS G-Walk device. Future research should include larger, more balanced cohorts, validate sensor reliability, and conduct longitudinal studies. Despite these limitations, the findings align with previous research and underscore the potential of inertial sensors in tailoring rehabilitation strategies and monitoring progress in KOA patients. Full article

Due to their high refractive index, record optical anisotropy and a set of excitonic transitions in visible range at a room temperature, transition metal dichalcogenides have gained much attention. Here, we adapted a femtosecond laser ablation for the synthesis of WSe₂ nanoparticles (NPs) with diameters from 5 to 150 nm, which conserve the crystalline structure of the original bulk crystal. This method was chosen due to its inherently substrate-additive-free nature and a high output level. The obtained nanoparticles absorb light stronger than the bulk crystal thanks to the local field enhancement, and they have a much higher photothermal conversion than conventional Si nanospheres. The highly mobile colloidal state of produced NPs makes them flexible for further application-dependent manipulations, which we demonstrated by creating substrates for SERS sensors. Full article

Handheld LiDAR scanners, which typically consist of a LiDAR sensor, Inertial Measurement Unit, and processor, enable data capture while moving, offering flexibility for various applications, including indoor and outdoor 3D mapping in fields such as architecture and civil engineering. Unlike fixed LiDAR systems, handheld devices allow data collection from different angles, but this mobility introduces challenges in data quality, particularly when initial calibration between sensors is not precise. Accurate LiDAR-IMU calibration, essential for mapping accuracy in Simultaneous Localization and Mapping applications, involves precise alignment of the sensors’ extrinsic parameters. This research presents a robust initial pose calibration method for LiDAR-IMU systems in handheld devices, specifically designed for indoor environments. The research contributions are twofold. Firstly, we present a robust plane detection method for LiDAR data. This plane detection method removes the noise caused by mobility of scanning device and provides accurate planes for precise LiDAR initial pose estimation. Secondly, we present a robust planes-aided LiDAR calibration method that estimates the initial pose. By employing this LiDAR calibration method, an efficient LiDAR-IMU calibration is achieved for accurate mapping. Experimental results demonstrate that the proposed method achieves lower calibration errors and improved computational efficiency compared to existing methods. Full article

Unmanned aerial vehicle (UAV)-enabled vehicular communications in the sixth generation (6G) are characterized by line-of-sight (LoS) and dynamically varying channel conditions. However, the presence of obstacles in the LoS path leads to shadowed fading environments. In UAV-assisted cellular vehicle-to-everything (C-V2X) communication, vehicle and UAV mobility and shadowing adversely impact latency and throughput. Moreover, 6G vehicular communications comprise data-intensive applications such as augmented reality, mixed reality, virtual reality, intelligent transportation, and autonomous vehicles. Since vehicles’ sensors generate immense amount of data, the latency in processing these applications also increases, particularly when the data are not independently identically distributed (non-i.i.d.). Furthermore, when the sensors’ data are heterogeneous in size and distribution, the incoming packets demand substantial computing resources, energy efficiency at the UAV servers and intelligent mechanisms to queue the incoming packets. Due to the limited battery power and coverage range of UAV, the quality of service (QoS) requirements such as coverage rate, UAV flying time, and fairness of vehicle selection are adversely impacted. Controlling the UAV trajectory so that it serves a maximum number of vehicles while maximizing battery power usage is a potential solution to enhance QoS. This paper investigates the system performance and communication disruption between vehicles and UAV due to Doppler effect in the orthogonal time–frequency space (OTFS) modulated channel. Moreover, a low-complexity UAV trajectory prediction and vehicle selection method is proposed using federated learning, which exploits related information from past trajectories. The weighted total energy consumption of a UAV is minimized by jointly optimizing the transmission window ($L_w$), transmit power and UAV trajectory considering Doppler spread. The simulation results reveal that the weighted total energy consumption of the OTFS-based system decreases up to 10% when combined with federated learning to locally process the sensor data at the vehicles and communicate the processed local models to the UAV. The weighted total energy consumption of the proposed federated learning algorithm decreases by 10–15% compared with convex optimization, heuristic, and meta-heuristic algorithms. Full article

This research presents the design and implementation of an Internet of Things (IoT)-based solution to measure the percentage of Liquefied Petroleum Gas (LPG) inside domestic stationary tanks. The IoT-based sensor, in addition to displaying the percentage of the LPG level in the tank to the user through a mobile application (app), has the advantage of simultaneously sharing the acquired data with an LPG filling plant via the Internet. The design process and calculations for the selection of the electronic components of the IoT-based sensor are presented. The methodology for obtaining and calibrating the measurement of the tank filling percentage from the magnetic level measurement system is explained in detail. The operation of the developed software, and the communication protocols used are also explained so that the data can be queried both in the user’s app and on the gas company’s web platform safely. The use of the Clark and Wright savings algorithm is proposed to sufficiently optimize the distribution routes that tank trucks should follow when serving different home refill requests from customers located in different places in a city. The experimental results confirm the functionality and viability of the hardware and software developed. In addition, by having the precise location of the tank, the generation of optimized gas refill routes for thirty customers using the heuristic algorithm and a visualization of them on Google Maps is demonstrated. This can lead to competitive advantages for home gas distribution companies. Full article