Search Results (4,604)

Stroke causes approximately 12.2 million new cases and 6.5 million deaths annually, with survivors requiring coordinated care across pre-hospital, acute, rehabilitative, and preventive phases. Mobile health (mHealth) technologies, including smartphone applications, wearable sensors, and tablet-based platforms, have shown clinical potential across these contexts, yet a structured mapping of their distribution across the full stroke care continuum is lacking. We searched PubMed, Scopus, and Web of Science for publications from January 2019 to March 2025. Studies evaluated mHealth interventions in which the mobile platform directly performed diagnostic, therapeutic, or rehabilitative functions in stroke populations. Of 4524 records identified, 17 met the inclusion criteria. Studies originated from eight countries and used heterogeneous designs: five randomized controlled trials, five non-randomized studies, four cohort studies, and three diagnostic accuracy studies. Median sample size was 37 participants (range 10–2249). Evidence concentrated at two poles: six studies addressed acute diagnosis and ten addressed rehabilitation, predominantly in the chronic phase. One study addressed secondary prevention; two targeted early rehabilitation, the period of maximum neuroplasticity after discharge. All seventeen studies covered a single care phase. Smartphone platforms dominated acute contexts; wearable and mixed-modality systems were confined to rehabilitation. The mHealth stroke landscape is fragmented and phase-specific, exhibiting a silo effect in which interventions operate as isolated tools rather than components of an integrated care system. An important gap is the near-absence of research in early rehabilitation. Future priorities include cross-continuum design, expansion into cognitive and secondary prevention domains, and progression toward adequately powered trials. Full article

Cage-based tilapia farming is highly vulnerable to rapid variations in water-quality parameters, particularly dissolved oxygen (DO) fluctuations, which can cause fish stress, fish mortality, and economic losses. In this study, we developed and field-evaluated an Internet of Things (IoT)- and LoRa-based water-quality-monitoring and aeration-actuation system for open-water tilapia cage farming. The system consists of distributed control nodes, a main node, a cloud database, and a mobile application for real-time monitoring of DO, pH, and water temperature, as well as remote and automatic oxygen-pump actuation. An automatic probe-lifting mechanism is integrated into the control node to reduce probe-submersion duration and mitigate the risk of sensor fouling during field operation. Field validation showed that the node equipped with the probe-lifting mechanism achieved better agreement with the reference instruments than the continuously submerged node, particularly for DO measurement, with RMSE values of 0.186 mg/L and 0.683 mg/L, respectively. A communication-performance evaluation showed 100% packet reception up to 1640 m, whereas packet reception was reduced at the longest tested distance of 2290 m, indicating that the field-deployment range should be interpreted cautiously under the tested LoRa configuration. Detection-latency experiments showed sub-second responsiveness, with average delays of 208.6–289.7 ms for single-hop communication and 438.9–529.4 ms for two-hop communication. Expert evaluation and farmer satisfaction assessment indicated positive perceptions of the system’s usability and practical relevance. However, the study has several limitations, including the short field-validation period, limited sensor replication, and a lack of direct fish production outcome measurements, which should be considered when interpreting the findings. Overall, the proposed system provides a practical platform for water-quality monitoring and aeration actuation in cage-based tilapia farming. Full article

This paper presents a wearable system based on low-cost inertial sensors for the continuous monitoring of driver motion and behavior under controlled urban driving conditions. The system consists of distributed wearable units placed on the head, neck, and torso, each equipped with an inertial measurement unit (IMU) that measures linear acceleration and angular velocity. The acquired data are processed in real time to characterize the driver’s kinematic response during vehicle operation. The main contribution of this work is the definition of a novel Driving Aggressiveness Index (DAI) for quantitative driving style assessment. The proposed index integrates motion-derived features based on acceleration and angular velocity and combines information from multiple body segments through a normalization and weighting strategy, enabling a compact and interpretable representation of driver behavior. Experimental validation was conducted in an urban driving scenario under controlled traffic-free conditions, including typical maneuvers such as straight driving, braking, roundabout navigation, lane changes, and yielding, performed under both normal and aggressive driving styles. The results demonstrate that the monitoring system captures distinct kinematic patterns and that the proposed index provides a clear and consistent separation between driving behaviors. A data-driven threshold is also defined, enabling the quantitative classification of driving styles. Overall, the proposed approach offers an interpretable, scalable, and real-time solution for driver monitoring, with potential applications in road safety and sustainable mobility. Full article

Anomaly detection performance in sensor data is highly sensitive to model hyperparameters, which is central to reliable monitoring in mobile Internet security and industrial IoT (IIoT) scenarios. We propose an IWOA-LightGBM-based anomaly detection method for sensor data. For machine learning-based anomaly detection methods, hyperparameter selection often determines model performance, so we propose an Improved Whale Optimization Algorithm (IWOA) and further use it to optimize the hyperparameters of the LightGBM algorithm. To avoid falling into local optima and accelerate algorithm convergence, the WOA is improved by integrating nonlinear convergence factor, adaptive inertia weight factor and stochastic differential mutation strategy. Experimental results show that during hyperparameter optimization for LightGBM model training, the IWOA achieves faster convergence and higher computational efficiency compared to the Whale Optimization Algorithm (WOA), with anomaly detection accuracy exceeding 90%. Full article

Urban navigation in dynamic environments remains a challenging problem for blind and visually impaired users due to the presence of unpredictable obstacles and the limitations of conventional navigation systems, which rely primarily on static map-based information and lack real-time environmental awareness. This paper presents a real-time sensor-driven navigation system based on a multi-agent architecture incorporating a reinforcement-learning navigation policy for assistive mobility in urban environments. The proposed system integrates GPS-based global localization with vision-based perception to enable continuous fusion of global route planning and local obstacle detection. This integration allows the system to dynamically adjust navigation strategies in response to changing environmental conditions. The architecture is designed as a modular multi-agent system comprising agents for perception, navigation, sensor fusion, personalization, safety arbitration, interface management, and system monitoring. The reinforcement learning component formulates local navigation as a sequential decision-making problem, where the navigation policy is trained to balance path efficiency, obstacle avoidance, and safety constraints through interaction with simulated environments. Prototype implementation is developed and evaluated in both simulation and controlled real-world scenarios. Experimental results demonstrate that the proposed system shows improved obstacle avoidance performance and navigation stability under the evaluated conditions while maintaining low-latency responsiveness compared to baseline navigation approaches. The system also exhibits robust behaviour under varying environmental conditions, supporting its potential applicability to assistive navigation tasks in controlled urban environments. The proposed approach contributes to a scalable architecture that integrates a reinforcement-learning navigation policy within a multi-agent coordination framework and real-time sensor perception, providing a foundation for the development of intelligent and deployable assistive navigation systems. Full article

Integrating depth sensors into mobile robots enables automated gait monitoring with potential applications in neurological disorders. This pilot study aims to evaluate the preliminary feasibility of robot-assisted gait assessment using Azure Kinect against Vicon, including individuals with multiple sclerosis, while simultaneously examining between-system, within-system, and environmental effects. A total of 20 participants were recruited to complete the eight-meter straight-line and 32 m corridor walking tests in the laboratory on the same day. Following independent data acquisition by both systems, temporal alignment was achieved through foot-event anchoring and interval trimming. On a unified timeline, 8 joint kinematic signals and 26 descriptors were extracted. Generalized estimating equations were applied, with a Bonferroni correction implemented for the 26 parallel tests to control the family error rate. The results showed: The spatiotemporal gait metrics exhibited general stability between systems and environments. Vicon better revealed variations in hip and pelvic amplitudes and restricted extension phenotypes, while the robotic system demonstrated greater sensitivity to knee posture and relative swing amplitude. The corridor environment induced an increase in stride length and a reduced step time compared to the laboratory, accompanied by a greater peak of hip and knee flexion and a greater forward lean of the trunk, with a largely preserved temporal organization. Within the Vicon-referenced framework, Azure Kinect-based robotic assessment demonstrated preliminary feasibility for capturing gait-related characteristics in individuals with multiple sclerosis. However, due to the limited number of analyzed MS participants, these findings should be interpreted as exploratory rather than as definitive clinical validation. The two systems exhibit complementary kinematic advantages. We recommend adopting an evaluation protocol that combines laboratory baseline with corridor validation, supplemented by descriptor-level mapping for cross-system data integration when necessary. This approach may support future tiered assessment, disease progression monitoring, and efficacy evaluation, but larger clinical cohorts are required to confirm its applicability in individuals with multiple sclerosis. Full article

Although compression therapy is widely used to improve wound healing, selecting the appropriate pressure remains a challenge in clinical practice. This work proposes an intelligent patch integrated into a bandage that allows for the simultaneous monitoring of the applied pressure and wound condition using Near-Field Communication (NFC). The proposed patch integrates a force-sensitive resistive sensor to measure pressure and a capacitive sensor to detect wound exudate through capacitance variations. Capacitance is obtained by analyzing the delay in the stepwise response of the sensor, while resistance is measured from the voltage drop across a resistive divider, which is read by a microcontroller’s analog-to-digital converter. The system is powered wirelessly through NFC energy harvesting, triggered by a mobile device that acts as a reader. The NFC module can be moved away after measurement to improve patient comfort or remain integrated into the dressing for periodic monitoring. Experimental results demonstrate pressure measurements up to 140 mmHg and exudate detection up to 200 $\mathsf{\mu }$L, confirming the feasibility of battery-free NFC smart bandages for therapeutic monitoring based on wound compression. Full article

The configuration of a multi-LiDAR system impacts coverage, redundancy, and observability in mobile mapping. In this study, a multi-LiDAR configuration is modeled as a constrained optimization problem that considers symmetry and clearance constraints. A physics-based simulation is applied to evaluate coverage, overlap, and angular diversity for spinning LiDARs such as the Ouster OS1-64 and the Velodyne VLP-16. Three methods of Bayesian Optimization (BO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are used. In an indoor space, all methods find symmetric multi-sensor configurations that maximize coverage and redundancy. GA and PSO methods required thousands of evaluations, whereas BO demonstrated excellent efficiency by converging in fewer iterations. Validation using simulated, realistic trajectories and ground-truth environments shows that symmetric multi-LiDAR configuration increases surface completeness by 10–11% over single-sensor setups (up to 27% for OS1-64 and 42% for VLP-16). The results further show that bilateral symmetry is a practical mounting constraint and also a robust design principle that improves mapping completeness. Full article

Background: Wearable activity trackers are commonly used in mHealth weight loss interventions, but evidence linking adherence to moderate-to-vigorous physical activity (MVPA) goals with changes in body composition is limited. We examined associations between adherence to study-prescribed MVPA goals and changes in percent body fat and sex-specific waist circumference (WC) over 12 months in the SMARTER trial. Methods: Participants (N = 502, 79.5% female; mean age 45 years; mean BMI 33.7 kg/m²) were randomized to self-monitoring of diet, PA, and weight (SM) or SM plus daily tailored feedback messages (SM + FB). Weekly adherence to ≥300 min/week of MVPA was quantified using Fitbit-derived equivalents. Associations between MVPA adherence and changes in percent body fat and sex-specific WC over 12 months were examined using linear mixed models. Results: Among the full sample, greater MVPA adherence was associated with reductions in body fat (b = −0.01; 95% CI: −0.02, −0.005), but not in WC (women: b = −0.01; −0.03, 0.01; men: b = −0.03; −0.05, 0.0002). Among the completers, higher adherence was associated with decreases in body fat (b = −0.01; −0.02, −0.004) and WC (women: b = −0.02; −0.04, −0.004; men: b = −0.04; −0.08, −0.003). Conclusions: Higher MVPA adherence was associated with favorable changes in adiposity over 12 months, supporting the use of wearable-derived PA measures in long-term mHealth behavioral interventions. Full article

The Indonesian government should seriously consider the use of renewable energy, given the natural potential that can still be utilized as an environmentally friendly power source. The utilization of renewable energy can be achieved by harnessing available natural resources. Pico hydro power plants (PLTPHs) can serve as an alternative electricity generator for use in Indonesia due to the existing natural potential. The output from this power plant can be utilized directly or stored in batteries. Directly measuring the generator’s performance on-site is deemed less effective. Therefore, a monitoring system is introduced as a solution to allow remote monitoring and display parameters such as voltage, current, frequency, and power of the generator online. This system is designed to display the micro hydro generator’s output parameter data on the Blynk application. The display on the Blynk application can be monitored via a connected mobile phone. Testing of the monitoring system was carried out by comparing two sets of measurements: one through the PZEM-004T sensor system and the other through a kWh meter (Kilowatt-hour meter). For the AC output from the battery with a 12-watt lamp load (tested 4 times), the reading error values obtained were a voltage reading error of 0.2%, a current reading error of 19.4%, a frequency reading error of 0.67%, and a power reading error of 18.2%. Full article

Quadrupedal wheel-legged robots possess exceptional mobility in complex terrains, but their robust locomotion control is severely hindered by the difficulty of accurate state estimation without external sensors. Existing reinforcement learning methods relying on two-stage imitation often suffer from representation collapse and information loss during sim-to-real transfer. To address these challenges, this paper proposes a novel end-to-end reinforcement learning framework for implicit state estimation, incorporating terrain and external force features. Inspired by internal model control, the proposed method leverages a history of purely proprioceptive observations to extract explicit kinematic responses, as well as implicit environmental and external force representations via prototypical contrastive learning, completely circumventing explicit terrain regression and the need for physical force sensors. Furthermore, a tailored composite reward function and a progressive curriculum training strategy with large-scale domain randomization are integrated to ensure dynamic stability and hardware safety. Extensive cross-simulator validations and real-world deployments demonstrate that the approach achieves highly agile and robust locomotion, including adaptive traversal over diverse terrains. Experiments show that the method significantly enhances robustness under external disturbances, notably reducing the lateral linear velocity tracking error from 0.2421 m/s to 0.1319 m/s. The proposed method realizes zero-shot sim-to-real transfer with superior sample efficiency, providing a reliable and universal control paradigm for wheel-legged robots in unstructured environments. Full article

Contemporary art conservation increasingly relies on digital technologies capable of delivering accurate, non-invasive documentation across multiple scales. Within this framework, the study addresses the challenges of documenting and monitoring artworks integrated into historical architectural contexts, proposing an interdisciplinary and need-driven approach where conservation requirements guide technological choices. The methodology combines four survey techniques (static and mobile laser scanning, photogrammetry, and structured-light acquisition) to evaluate their effectiveness within a multi-scale workflow supporting conservation-oriented documentation. The workflow is tested on the Centro per la Scultura Contemporanea in Cagli, Italy, a museum where contemporary installations are structurally and conceptually connected within the historical architectural space. The paper presents a comparative assessment of the four sensors, considering both qualitative and quantitative parameters. Comparative analyses of the resulting point clouds was carried out using cloud-to-cloud distance measurements with a terrestrial laser scanning dataset as reference. Error distribution and geometric deviations are assessed to evaluate the performance of each sensor according to the scale and purpose of the survey. The results demonstrate that accessible and portable instruments can produce datasets targeted at conservation processes, when integrated within coherent digital workflows, in which architectural, spatial, and object-scale models are combined to create a digital documentation framework. Full article

This article considers the causes of inter-column pressures (ICP) in wells and their impact on operational reliability. The analysis of Karachaganak field well stock for the period from 2001 to 2024 demonstrates that inter-column pressures manifest in a time frame of five to six years following drilling. These pressures are characterized by a spontaneous emergence and subsequent dissipation. This study proposes a mechanism where the formation of ICP is influenced by multiple factors, including cementing defects, as well as physical and chemical processes. Additionally, the geological heterogeneity of the section has been identified as a contributing factor. The results of studies employing a mobile laboratory and pumping unit are presented. The mobile laboratory unit (MLU) operates with pressure sensors in the range of 0–100 MPa (accuracy ±0.5%), a pump rate of 0.5–20 L/min, and an injection pressure up to 70 MPa; fluid sampling is performed by a discrete sampler with a volume of 500 mL. These allow the identification of sources and channels of fluid migration into the inter-column space, as well as the carrying out of technological operations to reduce and eliminate ICP. This paper sets out a risk-oriented method of inter-column pressure assessment. The proposed risk-based method classifies wells into three risk levels (low, medium, high) based on a composite index R = (P/Pmax) + (V/Vmax) + (C/Cmax) where P is annulus pressure, V is escaped fluid volume per day, C is concentration of H₂S, CO₂, or mercaptan, respectively, and threshold values are Pmax = 35 MPa (API RP 90), Vmax = 50 m³/day, and Cmax = 10 ppm for H₂S. This method takes into account not only the pressure value, but also the volume of escaping fluid and the concentration of aggressive components. It is concluded that an integrated approach to diagnostics and management of inter-column pressures is necessary. This approach should be supported by technological solutions that ensure increased reliability and environmental safety of well operation. Full article

High-performance flexible electronics require soft materials that combine mechanical robustness with efficient ionic conduction. In conventional ionogels, however, these requirements often conflict: dense networks improve strength but reduce the free volume and mobility needed for ion transport. This review provides a critical overview of recent progress in cellulose-based ionogels, with emphasis on design principles for decoupling mechanical and conductive properties. We discuss how cellulose precursors, crosslinking architectures (hydrogen bonding, covalent networks, and metal-ion coordination), and processing histories determine gel structure and mechanical integrity. We then highlight strategies that mitigate the trade-off, including precursor engineering, phase-separated networks, double-network architectures, crystallization-induced reorganization, and anisotropic assembly. Representative applications in flexible sensors, flexible energy-storage devices, and soft actuators are also summarized. This review offers a practical framework for designing cellulose-based soft functional materials with robust mechanics and sustained ionic conductivity. Full article

Falls in older adults and individuals with balance impairments remain a major concern because they are closely associated with injury, reduced mobility, and loss of independence. This study presents a preclinical proof-of-concept for a cognitive smart walker architecture that combines user-compatible walking assistance with active safety intervention. The system integrates a 2D LiDAR sensor for contactless lower-limb monitoring, a six-degree-of-freedom (6-DOF) force/torque sensor to measure user–walker interaction, and an inertial measurement unit (IMU) for dynamic monitoring, with all data processed in real time on a Raspberry Pi/ROS-based platform. Normal walking assistance is provided through a command-level variable admittance-based controller that converts interaction forces into a smoothed signed duty-cycle command rather than a rigid speed-control signal. Safety decisions are managed by a Hierarchical State Machine (HSM). Early-risk conditions are handled through motor-based dynamic braking, whereas severe physical crises additionally deploy lateral support legs to enlarge the base of support. Within this framework, the system can detect and manage foot entanglement, grip loss, forward fall, vertical collapse, lateral fall, successive crises, and recovery-abort events. In experiments across multiple scenarios, the system correctly detected all 50 crisis cases and did not issue unnecessary interventions in 30 non-crisis cases. These findings show that the proposed architecture can preserve transparent walking assistance during normal gait while providing graded, context-sensitive active safety when risk emerges. Full article