Search Results (510)

Today, there is a high demand for remote rehabilitation using mobile robotic complexes all over the world. They offer a wide range of options for convenient and effective therapy at home to patients and the elderly, especially those bedridden after musculoskeletal injuries. In this case, modern approaches to the development of exoskeletons for the rehabilitation of the lower extremities are especially relevant for the effective restoration of lost motor functions. Taking into account the advantages and features of robotic rehabilitation, this work is devoted to the development of a prototype exoskeleton for the ankle joint and experimental studies of the remote control module. The proposed new exoskeleton prototype design was integrated with a mobile wireless communication platform, allowing remote control of the position of the exoskeleton foot using a remote control device. As a result of functional testing, the root mean square error (RMSE) was 23.9° for dorsiflexion/plantarflexion movements and 12.8° for inversion and eversion movements, as well as an average signal transmission delay of about 100 ms and packet loss of 0.6%. These results reflect the technical feasibility of remote control at a distance of up to 10 m. The developed system is mobile, autonomous, and easy to use, which confirms its suitability as a laboratory platform for functional verification and testing of module consistency. Full article

As terrestrial oil and gas resources continue to decline, deep-sea oil and gas development has become a strategic priority. A wide range of production equipment must be deployed on the seabed, among which subsea wet-mateable connectors are indispensable. To address the challenges of high pressure, low temperature, and corrosion in deep-sea environments, this study proposes a cooperative sealing strategy between the annular protrusion on the entry casing and a sliding sleeve. The leakage per single mate/demate cycle is quantified under varying insertion speeds and pressure differentials. By examining the effects of protrusion geometry, insertion speed, friction coefficient, and radial compression on sealing performance, the optimal parameters are identified: a friction coefficient of 0.15 and a trapezoidal-rib seal with 0.015 mm radial compression for dynamic sealing, yielding a contact pressure of 27.5 MPa and a mating/demating force of 197.26 N—satisfying the manipulation requirements of a remotely operated vehicle. Hydrostatic pressure tests demonstrate that the dynamic sealing design of the underwater connector achieves a balance between high reliability and low insertion resistance, and the prototype meets the operational requirements for deep-sea service. Full article

Water scarcity is a relevant issue whose impact can be mitigated through sustainable solutions. Humidification–dehumidification (HDH) cycles powered by photovoltaic thermal (PVT) modules enable pure water production in remote areas. In this study, models have been developed and validated for the main components of the system, the humidifier and the dehumidifier. A unique HDH-PVT prototype was built and experimentally tested at the SolarTech Lab of Politecnico di Milano in Milan, Italy. The experimental system is a Closed Air Closed Water—Water Heated (CACW-WH) that mimics a Closed Air Open Water—Water Heated (CAOW-WH) cycle through brine cooling, pure water mixing, and recirculation, avoiding a continuous waste of water. Tests were performed varying the mass flow ratio (MR) between 0.346 and 2.03 during summer and autumn in 2023 and 2024. The experimental results enabled the verification of the developed models. The optimal system performance was obtained for an MR close to 1 and a maximum cycle temperature of $44 °\mathrm{C}$, enabling a 0.51 gain output ratio (GOR) and 0.72% recovery ratio (RR). The electrical and thermal energy generation of the PVT modules satisfied the whole consumption of the system enabling pure water production exploiting only the solar resource available. The PVT-HDH system proved the viability of the proposed solution for a sustainable self-sufficient desalination system in remote areas, thus successfully addressing water scarcity issues exploiting a renewable energy source. Full article

This work presents an Internet of Things (IoT) node designed for low-power agrifood chain tracking in remote areas, where long-range terrestrial communication is either unavailable or severely limited. The novelty of this study lies in the development and characterization of an IoT node prototype that leverages direct-to-satellite connectivity through a geostationary Earth orbit (GEO) satellite, using long-range frequency-hopping spread spectrum (LR-FHSS) modulation in the licensed S-band. The prototype integrates a microcontroller unit that manages both the radio modem and a suite of sensors, enclosed in a plastic box suitable for field deployment. Characterization in an anechoic chamber demonstrated a maximum effective isotropic radiated power (EIRP) of $27.5$ dBm, sufficient to establish a reliable satellite link. The onboard sensors provide global positioning as well as measurements of acceleration, temperature, humidity, and solar radiation intensity. Prototype performance was assessed in two representative scenarios: stationary and mobile. Regarding energy consumption, the average charge drained by the radio modem per transmission cycle was measured to be 356 mC. With a battery pack composed of four 2500 mAh NiMH cells, the estimated upper bound on the number of transmitted packets is approximately 25,000. Full article

This paper introduces an open-source software framework for implementing Field-Oriented Control (FOC) on a Brushless DC Motor (BLDC) across its entire speed range. The control strategy employs a Direct FOC method with a single Hall sensor combined with Space Vector Pulse Width Modulation (SVPWM) and complementary sensorless techniques. The BLDC motor and supporting circuits are modeled and validated through both simulation and hardware implementation. A modular software architecture enables deployment via distinct system components, promoting hardware abstraction and reducing platform-specific dependencies. The entire setup is conceptualized and executed in MATLAB/Simulink R2024b and the framework supports remote experimentation through a web-based interface, requiring only a single MATLAB license. This scalable solution is designed for academic researchers and industry practitioners alike, offering an accessible low-cost platform for motor control development, validation, and early-stage prototyping. Full article

Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of ≥95% and an atrial fibrillation detection sensitivity/specificity of ≥96%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools. Full article

This paper presents the results of seakeeping tests conducted on the Tritor, a remotely controlled autonomous unmanned surface vehicle (USV) featuring a trimaran hull design known as the Three Slender Cylinders Hull (3SCH) and equipped with electric propulsion. Previous research focused on the vehicle’s design, prototype development, and initial functional testing. Tritor is characterised by its simple design and construction, reliable propulsion system, and excellent stability and manoeuvrability. Its control and navigation systems have demonstrated effective performance in both remote-controlled and fully autonomous modes. In the present study, seakeeping tests were carried out in a towing tank, with repeated trials conducted at various speeds and wavelengths. The selected wavelengths were close to the vehicle’s length, where the most significant responses were expected. Test speeds ranged from 1.0 to 2.5 m per second, based on prior operational experience with the vehicle. Due to the constraints of the towing tank, all wave directions were limited to head seas. Measurements included heave and pitch motions. Vertical accelerations at the vehicle’s centre of gravity were derived from the heave data and used as a key indicator of seakeeping performance. The results were evaluated against established seakeeping criteria related to vessel operability and structural safety. Full article

This article proposes an improved Visible Light Communication (VLC) solution that, besides the indoor lighting and data transfer, offers an energy-efficient alternative for modern workspaces. Unlike Light-Fidelity (LiFi), designed for high-speed data communication, VLC primarily targets applications where fast data rates are not essential. The developed prototype ensures reliable communication under variable lighting conditions, addressing low-speed requirements such as test bench monitoring, occupancy detection, remote commands, logging or access control. Although the tested data rate was limited to 100 kb/s with a Bit Error Rate (BER) below 10⁻⁷, the key innovation is the light dimming dynamic adaptation. Therefore, the system self-adjusts the LED duty cycle between 10% and 90%, based on natural or artificial ambient light, to maintain a minimum illuminance of 300 lx at the workspace level. Additionally, this work includes a scalability analysis through simulations conducted in an office scenario with up to six users. The results show that the system can adjust the lighting level and maintain the connectivity according to users’ presence, significantly reducing energy consumption without compromising visual comfort or communication performance. With this light intensity regulation algorithm, the proposed solution demonstrates real potential for implementation in smart indoor environments focused on sustainability and connectivity. Full article

We developed a forecast model for cyanobacteria bloom formation in two contrasting inland lakes in Australia by combining in situ sampling and continuous remote sensing hyperspectral reflectance (HydraSpectra) with hydrodynamic and algal growth models. Cyanobacterial distribution of a buoyant species was simulated with an algal growth model, driven by forecasted meteorological data, and modeled temperature stratification and mixing dynamics from a one-dimensional, vertical k-epsilon turbulence hydrodynamic model. The cyanobacteria model was re-initialized daily with measured cell counts derived from the hyperspectral reflectance data. Simulations of cyanobacterial concentrations (cell counts) reflected the dynamic mixing behavior in the lakes with daily phases of near-surface accumulation and subsequent daily mixing due to wind or night-time cooling. To determine the surface concentration of cyanobacteria on sub-daily time scales, it was demonstrated that the combined use of high-resolution water temperature profiles, HydraSpectra reflectance data, and a hydrodynamic model to quantify the mixing dynamics is essential. Overall, the model results demonstrated a prototype for a cyanobacteria short-term forecast model. Having these tools in place allows us to quantify the risks of cyanobacterial blooms in advance to inform options for lake management. Full article

Sudden Infant Death Syndrome (SIDS) is one of the leading causes of postnatal mortality, with the prone sleeping position identified as a critical risk factor. This article presents the design, implementation, and validation of a low-cost embedded system for unobtrusive, real-time monitoring of infant posture. The system acquires data from a pressure mat on which the infant rests, converting the pressure matrix into an image representing the postural imprint. A Convolutional Neural Network (CNN) has been trained to classify these images and distinguish between prone and supine positions with high accuracy. The trained model was optimized and deployed in a data acquisition and processing system (DAQ) based on the Raspberry Pi platform, enabling local and autonomous inference. To prevent false positives, the system activates a visual and audible alarm upon detection of a sustained risk position, alongside remote notifications via the MQTT protocol. The results demonstrate that the prototype is capable of reliably and continuously identifying the infant’s posture when used by people who are not technology experts. We conclude that it is feasible to develop an autonomous, accessible, and effective monitoring system that can serve as a support tool for caregivers and as a technological basis for new strategies in SIDS prevention. Full article

Recently, the development of smartphone apps has resulted in a wide range of services being offered related to wood supply chain management, supporting decision-making and narrowing the digital divide in this business. This study examined the performance of Tree Scanner (TS)—a LiDAR-based smartphone app prototype integrating advanced algorithms—in estimating and providing instant data on log volume through direct digital measurement. Digital log measurements were conducted by two researchers, who each performed two repetitions; in addition to accuracy, measurement-time efficiency was also considered in this study. The results indicate strong agreement between the standard (manual) and digital measurement estimates, with an R² > 0.98 and a low RMSE (0.0668 m³), as well as intra- and inter-user consistency. Moreover, the app showed significant potential for productivity improvement (38%), with digital measurements taking a median time of 21 s per log compared to 29 s per log with manual measurements. Its ease of use and integration of several key functionalities—such as Bluetooth transfer, remote server services, automatic species identification, the provision of instant volume estimates, compatibility with RFID tags and wood anatomy checking devices, and the ability to document the geographic location of measurements—make the Tree Scanner app a useful tool for integration into wood traceability systems. Full article

This paper presents a real-time smart mirror system combining multiple AI modules for multimodal health monitoring. The proposed platform integrates three core components: facial expression analysis, remote photoplethysmography (rPPG), and conversational AI. A key innovation lies in transforming the Moving Average rPPG (MA-rPPG) model—originally developed for offline batch processing—into a real-time, continuously streaming setup, enabling seamless heart rate and peripheral oxygen saturation (SpO₂) monitoring using standard webcams. The system also incorporates the DeepFace facial analysis library for live emotion, age detection, and a Generative Pre-trained Transformer 4o (GPT-4o)-based mental health chatbot with bilingual (English/Korean) support and voice synthesis. Embedded into a touchscreen mirror with Graphical User Interface (GUI), this solution delivers ambient, low-interruption interaction and real-time user feedback. By unifying these AI modules within an interactive smart mirror, our findings demonstrate the feasibility of integrating multimodal sensing (rPPG, affect detection) and conversational AI into a real-time smart mirror platform. This system is presented as a feasibility-stage prototype to promote real-time health awareness and empathetic feedback. The physiological validation was limited to a single subject, and the user evaluation constituted only a small formative assessment; therefore, results should be interpreted strictly as preliminary feasibility evidence. The system is not intended to provide clinical diagnosis or generalizable accuracy at this stage. Full article

Biosensors have undergone transformative advancements, evolving into sophisticated wearable and implantable devices capable of real-time health monitoring. Traditional manufacturing methods, however, face limitations in scalability, cost, and design complexity, particularly for miniaturized, multifunctional biosensors. The integration of 3D printing technology addresses these challenges by enabling rapid prototyping, customization, and the production of intricate geometries with high precision. This review explores how additive manufacturing techniques facilitate the fabrication of flexible, stretchable, and biocompatible biosensors. By incorporating advanced materials like conductive polymers, nanocomposites, and hydrogels, 3D-printed biosensors achieve enhanced sensitivity, durability, and seamless integration with biological systems. Innovations such as biodegradable substrates and multi-material printing further expand applications in continuous glucose monitoring, neural interfaces, and point-of-care diagnostics. Despite challenges in material optimization and regulatory standardization, the convergence of 3D printing with nanotechnology and smart diagnostics heralds a new era of personalized, proactive healthcare, offering scalable solutions for both clinical and remote settings. This synthesis underscores the pivotal role of additive manufacturing in advancing wearable and implantable biosensor technology, paving the way for next-generation devices that prioritize patient-specific care and real-time health management. Full article

Background: The incorporation of biosensors into orthopedic implants, particularly tibial components in total knee arthroplasty (TKA), marks a new era in personalized joint replacement. These smart systems aim to provide real-time physiological and mechanical data, enabling dynamic postoperative monitoring and enhanced surgical precision. Objective: This narrative review synthesizes the current landscape of electrochemical biosensor-embedded tibial implants in TKA, exploring technical mechanisms, clinical applications, challenges, and future directions for translation into clinical practice. Methods: A comprehensive literature review was conducted across PubMed and Google Scholar. Articles were thematically categorized into technology design, integration strategies, preclinical and clinical evidence, regulatory frameworks, ethical considerations, and strategic recommendations. Findings were synthesized narratively and organized to support forward-looking system design. Results: Smart tibial implants have demonstrated feasibility in both bench and early clinical settings. Key advances include pressure-sensing intraoperative tools, inertial measurement units for remote gait tracking, and chemical biosensors for infection surveillance. However, the field remains limited by biological encapsulation, signal degradation, regulatory uncertainty, and data privacy challenges. Interdisciplinary design, standardized testing, translational funding, and ethical oversight are essential to scaling these innovations. Conclusions: Biosensor-enabled tibial components represent a promising convergence of orthopedics, electronics, and data science. By addressing the technological, biological, regulatory, and ethical gaps outlined herein, this field can transition from prototype to widespread clinical reality—offering new precision in arthroplasty care. Full article

This paper presents an intelligent communication architecture, designed to manage multiple power devices operating within a shared Low-Voltage Direct Current (LVDC) bus. These devices act either as energy consumers, e.g., Electric Vehicle (EV) chargers, Power Distribution Units (PDUs), or as sources and regulators, e.g., Alternating Current-to-Direct Current (AC/DC) converters, energy storage system (ESS) units. Communication is established using industrial protocols such as Modular Digital Bus (MODBUS) over Transmission Control Protocol (TCP) or Remote Terminal Unit (RTU), and Controller Area Network (CAN). The proposed system supports both data acquisition and configuration of field devices. It exposes their information to an Energy Management System (EMS) via a MODBUS TCP server. A key contribution of this work is the integration of a lightweight Machine Learning (ML)-based data prioritization mechanism that dynamically adjusts the update frequency of each MODBUS parameter based on its current relevance. This ML-based method has been prototyped and evaluated within a virtualized Internet of Things (IoT) gateway environment. It enables real-time, efficient, and scalable communication without altering the EMS or disrupting legacy protocol operations. Furthermore, the proposed approach allows for early testing and validation of the prioritization strategy before full hardware integration in the demonstrators planned as part of the SHIFT2DC project under the Horizon Europe program. Full article