Search Results (2,790)

Wireless Body Area Networks (WBANs) enable real-time data collection for medical monitoring, sports tracking, and environmental sensing, driven by Internet of Things advancements. Their layered architecture supports efficient sensing, aggregation, and analysis, but energy constraints from transmission (over 60% of consumption), idle listening, and dynamic conditions like body motion hinder adoption. Challenges include minimizing energy waste while ensuring data reliability, Quality of Service (QoS), and adaptation to channel variations, alongside algorithm complexity and privacy concerns. This paper reviews recent power control mechanisms in WBANs, encompassing feedback control, dynamic and convex optimization, graph theory-based path optimization, game theory, reinforcement learning, deep reinforcement learning, hybrid frameworks, and emerging architectures such as federated learning and cell-free massive MIMO, adopting a systematic review approach with a focus on healthcare and IoT application scenarios. Achieving energy savings ranging from 6% (simple feedback control) to 50% (hybrid frameworks with emerging architectures), depending on method complexity and application scenario, with prolonged network lifetime and improved reliability while preserving QoS requirements in healthcare and IoT applications. Full article

This study presents the design, implementation, and experimental validation of an integrated fastening monitoring platform for vehicle ground equipment, aimed at supporting structural maintenance and operational safety. Rather than introducing a fundamentally new sensing principle, the work focuses on the system-level integration and verification of existing sensing, communication, and control technologies for reliable bolt loosening detection and torque-controlled pneumatic fastening. The proposed platform consists of a Smart Control Gateway (SCG), a Signal Transducer Socket (STS), and a Smart Washer Set (SWS), incorporating smart nuts and clamping-force sensing washers for M50 and M35 bolts. Sub-GHz wireless RF communication and wired RS-485 transmission are employed to provide scalable and robust connectivity among system components. The SCG hardware and firmware are fully implemented and verified, enabling continuous acquisition and transmission of fastening-state data. Experimental evaluations include functional verification, mechanical integration tests, and durability assessments. The smart washers demonstrate stable sensing performance over 100 assembly and disassembly cycles without observable degradation. The STS is validated through 200,000 impact cycles under intermittent loading conditions (3 s impact, 3 s pause), confirming its suitability for repeated industrial operation. Real-time data transmission tests verify the system’s capability to detect bolt loosening events induced by vibration or external interference. The results indicate that the proposed platform provides a practical and reliable solution for fastening-state monitoring in safety-relevant ground equipment. This work contributes validated engineering evidence for deploying integrated smart fastening systems in industrial maintenance applications and establishes a foundation for future studies on environmental robustness, false-alarm characterization, and real-time performance guarantees. Full article

The popularity of unmanned vehicles in numerous areas of employment, combined with the diversity and continuing evolution of their payloads, make the communication solutions utilized by such vehicles the element of a particular importance. In our previous publication, we confirmed a general applicability of wireless local area network (WLAN) technologies as solutions suitable to provide a control loop communication of unmanned surface vehicles (USVs). At the same time, our research indicated that WLAN technologies provide communication resources in excess of what is required for the above task. In this paper, we aim to verify if a WLAN-based USV communication solution can be reliably utilized for both time-sensitive control loop and high-throughput payload communication simultaneously, which could provide significant advantages during USV construction and operation. For this purpose, we analyzed traffic parameters of popular USV payloads, designed a test system to monitor the impact of such traffic sharing a WLAN link with a USV control loop communication and conducted laboratory and field experiments. As initial results indicated the significant impact of payload traffic on the quality of control communication, we have also proposed a method of employing Commercial Off The Shelf (COTS) hardware for this purpose, in a manner which allows the above-mentioned link sharing to operate reliably in changing real-world conditions. The subsequent verification, first in the laboratory and then during a real-world USV field deployment, confirmed the effectiveness of the proposed method. Full article

Background: Congenital Insensitivity to Pain and Anhidrosis (CIPA) is a rare autosomal recessive disorder characterized by congenital analgesia, anhidrosis, and multisystem involvement affecting the musculoskeletal, cutaneous, oral, and para-oral structures. This case report describes the oral phenotype and multidisciplinary clinical management of a child with CIPA. Case Description: A 9-year-old boy presented with poor oral hygiene, multiple severely damaged teeth, masticatory difficulty, limited mouth opening, impaired bolus control, and para-oral traumatic injuries. Medical and orthopedic history indicated recurrent painless fractures, self-inflicted injuries, cutaneous scarring, and recurrent hyperpyrexia. Oral self-injury associated with CIPA was suspected and supported by the Nociception Assessment Test and Minor’s Iodine–Starch Test. Although the clinical findings were suggestive of CIPA, the diagnosis remained presumptive due to the absence of confirmatory molecular or histopathological testing. Management: A wearable wireless continuous temperature-monitoring device was prescribed to assist in tracking hyperpyrexia associated with CIPA (RHA-CIPA). A conservative, staged, multidisciplinary treatment was planned rather than full-mouth extraction, emphasizing prevention of dental sepsis and mitigation of future self-injury. Dental procedures were performed under local anesthesia to manage discomfort related to tactile hyperesthesia. To reduce nocturnal biting and oral trauma, a hard acrylic occlusal protector was fabricated using an intraoral scanner and a 3D-printed cast. The patient was followed for 12 months. Outcomes: At the 12-month follow-up, clinical improvement was observed, with particularly notable gains in cheek elasticity and soft tissue resilience. Conclusions: This case highlights the considerable challenges involved in the interdisciplinary management of children with CIPA, including oral self-injury prevention, limited mouth opening, and the necessity of close coordination with medical specialties. These findings are descriptive observations of a single case and do not establish efficacy or generalizability of any intervention. Full article

Background: Neuromuscular electrical stimulation (NMES) is an effective exogenous neuromuscular activation method widely used in sports training and rehabilitation. However, existing research primarily focuses on land-based sports or single-joint movements, with limited in-depth exploration of its intervention effects and underlying neuromuscular control mechanisms for complex, multi-joint coordinated aquatic activities like breaststroke swimming. This study aimed to investigate the effects of NMES combined with traditional resistance training on neuromuscular function during sport-specific technical movements in breaststroke athletes. Methods: A randomized controlled trial was conducted with 30 national-level or above breaststroke athletes assigned to either an experimental group (NMES combined with traditional squat resistance training) or a control group (traditional squat resistance training only) for an 8-week intervention. A specialized fully waterproof wireless electromyography (EMG) sensor system (Mini Wave Infinity Waterproof) was used to synchronously collect surface EMG signals from 10 lower limb and trunk muscles during actual swimming, combined with high-speed video for movement phase segmentation. Changes in lower limb explosive power were assessed using a force plate. Non-negative matrix factorization (NMF) muscle synergy analysis was employed to compare changes in muscle activation levels (iEMG, RMS) and synergy patterns (spatial structure, temporal activation coefficients) across different phases of the breaststroke kick before and after the intervention. Results: Compared to the control group, the experimental group demonstrated significantly greater improvements in single-leg jump height (Δ = 0.06 m vs. 0.03 m) and double-leg jump height (Δ = 0.07 m vs. 0.03 m). Time-domain EMG analysis revealed that the experimental group showed more significant increases in iEMG values for the adductor longus, adductor magnus, and gastrocnemius lateralis during the leg-retraction and leg-flipping phases (p < 0.05). During the pedal-clamp phase, the experimental group exhibited significantly reduced activation of the tibialis anterior alongside enhanced activation of the gastrocnemius. Muscle synergy analysis indicated that post-intervention, the experimental group showed a significant increase in the weighting of the vastus medialis and biceps femoris within synergy module 4 (SYN4, related to propulsion and posture) (p < 0.05), a significant increase in rectus abdominis weighting within synergy module 3 (SYN3, p = 0.033), and a significant shortening of the activation duration of synergy module 2 (SYN2, p = 0.007). Conclusions: NMES combined with traditional resistance training significantly enhances land-based explosive power in breaststroke athletes and specifically optimizes neuromuscular control strategies during the underwater breaststroke kick. This optimization is characterized by improved activation efficiency of key muscle groups, more economical coordination of antagonist muscles, and adaptive remodeling of inter-muscle synergy patterns in specific movement phases. This study provides novel evidence supporting the application of NMES in swimming-specific strength training, spanning from macroscopic performance to microscopic neural control. Full article

Monitoring muscle fatigue is essential to ensure safety and support activity in populations such as the elderly. This study introduces a novel deep learning framework for classifying muscle fatigue levels using data from wireless surface electromyographic sensors, with the long-term goal of supporting applications in Ambient Assisted Living. A new dataset was collected from healthy elderly and non-elderly adults performing dynamic tasks under controlled conditions, with muscle fatigue levels labelled through self-assessment. The proposed method employs a pipeline that transforms one-dimensional electromyographic signals into two-dimensional time–frequency images (scalograms) using the Continuous Wavelet Transform, which are then classified by a fine-tuned, pre-trained Convolutional Neural Network. These images are then classified by pretrained Convolutional Neural Networks on large-scale image datasets. The classification pipeline includes an initial binary discrimination between non-fatigued and fatigued conditions, followed by a refined three-level classification into No Fatigue, Moderate Fatigue, and Hard Fatigue. The system achieved an accuracy of 98.6% in the binary task and 95.6% in the multiclass setting. This integrated transfer learning pipeline outperformed traditional Machine Learning methods based on manually extracted features, which reached a maximum of 92% accuracy. These findings highlight the robustness and generalizability of the proposed approach, supporting its potential as a real-time, non-invasive muscle fatigue monitoring solution tailored to Ambient Assisted Living scenarios. Full article

Objective: to clarify differences in the intermuscular coherence of core muscles during side kicks among male Sanda athletes at varying skill levels, particularly in critical frequency bands; to reveal the association between neuromuscular coordination mechanisms and technical proficiency; and to provide methodological references for quantitative analysis of combat sports techniques. Methods: Thirty-six male Sanda athletes were divided into professional (n = 18) and amateur (n = 18) groups based on athletic ranking and training duration. Surface electromyographic (EMG) signals from 15 core muscles and kinematic data were synchronously recorded using a wireless EMG system and a high-speed camera. Signal processing extracted root mean square amplitude (RMS) and integral EMG (iEMG). Muscle coordination was quantified via time-frequency coherence analysis across alpha (8–15 Hz), beta (15–30 Hz), and gamma (30–50 Hz) bands. Results: The professional group exhibited significantly higher RMS and iEMG values in most core muscles (e.g., rectus femoris RMS: 0.298 ± 0.072 vs. 0.214 ± 0.077 mV, p < 0.001). Regarding intermuscular coherence, the professional group demonstrated significantly superior coherence in the α, β, and γ bands for key muscle pairs, including upper limb–swing leg, support leg–swing leg, and upper limb–support leg. Notable differences were observed in pairs such as external oblique–rectus femoris (alpha band: 0.039 ± 0.012 vs. 0.032 ± 0.011, p < 0.01) and right rectus femoris–biceps femoris (beta band: 0.033 ± 0.010 vs. 0.023 ± 0.007, p < 0.01). Conclusions: The fundamental difference in side kick technique among Sanda athletes lies in neuromuscular control strategies and muscle coordination efficiency. Sensor-based intermuscular coherence analysis provides an objective quantitative indicator for distinguishing technical proficiency, offering a scientific basis for optimizing training and extending the methodological framework for technique assessment in combat sports. Full article

Wireless Sensor Networks (WSNs) operate under strict energy constraints and are therefore highly vulnerable to radio interference, particularly jamming attacks that directly affect communication availability and network lifetime. Although jamming and anti-jamming mechanisms have been extensively studied, energy is frequently treated as a secondary metric, and analyses are often conducted in partial isolation from system assumptions, protocol behavior, and deployment context. This fragmentation limits the interpretability and comparability of reported results. This article presents a systematic literature review (SLR) covering the period from 2004 to 2024, with a specific focus on energy-aware jamming and mitigation strategies in IEEE 802.15.4-based WSNs. To ensure transparency and reproducibility, the literature selection and refinement process is formalized through a mathematical search-and-filtering model. From an initial corpus of 482 publications retrieved from Scopus, 62 peer-reviewed studies were selected and analyzed across multiple dimensions, including jamming modality, affected protocol layers, energy consumption patterns, evaluation assumptions, and deployment scenarios. The review reveals consistent energy trends among constant, random, and reactive jamming strategies, as well as significant variability in the energy overhead introduced by defensive mechanisms at the physical (PHY), Medium Access Control (MAC), and network layers. It further identifies persistent methodological challenges, such as heterogeneous energy metrics, incomplete characterization of jamming intensity, and the limited use of real-hardware testbeds. To address these gaps, the paper introduces an energy-centric taxonomy that explicitly accounts for attacker–defender energy asymmetry, cross-layer interactions, and recurring experimental assumptions, and proposes a minimal set of standardized energy-related performance metrics suitable for IEEE 802.15.4 environments. By synthesizing energy behaviors, trade-offs, and application-specific implications, this review provides a structured foundation for the design and evaluation of resilient, energy-proportional WSNs operating under availability-oriented adversarial interference. Full article

Wireless power transfer (WPT) has emerged as a key enabling technology for the large-scale adoption of electric vehicles (EVs), offering enhanced charging flexibility, improved safety, and seamless integration with intelligent transportation and renewable energy infrastructures. This paper presents a comprehensive review and technical synthesis of WPT technologies spanning both near-field and far-field domains, including inductive power transfer (IPT), magnetically coupled resonant WPT (MCR-WPT), capacitive power transfer (CPT), microwave power transfer (MPT), and laser wireless charging (LPT). Particular emphasis is placed on MCR-WPT, the most widely adopted approach for EV wireless charging, for which the coupler structures, resonant compensation networks, power converter architectures, and control strategies are systematically analyzed. The review further identifies that hybrid WPT architectures, adaptive compensation design and wide-coverage coupling mechanisms will be central to enabling high-power, long-distance, and misalignment-resilient wireless charging solutions for next-generation electric transportation systems. Full article

New wearable technology opens new possibilities for low-cost, easily accessible home-based interventions as a supplement to typical clinical rehabilitation therapy. In this pilot study, we tested a new interactive adjustable Feedback training system on 14 infants at high risk of cerebral palsy between 2 and 12 months of age to facilitate increased movements. The system consists of four wireless motion sensors placed on the infant’s limbs. Inertial sensors track the infant’s movements which control auditory and visual stimuli that act as motivational feedback. A 15 min usage of the Feedback training system four days a week for approximately six months was aimed for. None of the participants reached the recommended amount of intervention, due to time limitations. Seven of the twelve participating infants (58%) achieved at least 50% of the recommended training amount. Parents found the Feedback training system easy to use with minimal need for technical assistance. Preliminary data suggest that infants engaged more actively during training sessions where their movements actively controlled the presentation of the stimuli. The Feedback training system is promising as a user-friendly add-on to the playful and interactive stimulation of motor and cognitive development in infants with neurodevelopmental disorders. Full article

This paper proposes a dual path mixed coupling wireless power transfer (DPMPT) coupler as a four-port structure for near-field wireless power transfer in drone and unmanned aerial vehicles. The DPMPT coupler integrates orthogonal double-D coils and 8-plates to realize mixed inductive–capacitive coupling at 6.78 MHz without additional lumped matching networks. A four-port equivalent model is developed by classifying the mutual networks into three coupling types and representing them with a transmission-matrix formulation fitted to three-dimensional full-wave simulations. The model is used to identify the main coupling paths and to evaluate the effect of rotation and lateral/diagonal misalignment on power-transfer characteristics. Simulation results at a transfer distance of 70 mm show a maximum transmission coefficient of about 0.82 at 6.78 MHz and high robustness against rotation. When switch-based port selection is applied on the transmit side, blind spots associated with pose variations that cause an abrupt drop in transmission characteristics are significantly reduced, demonstrating that the DPMPT coupler with switch control provides an effective structural basis for enhancing alignment tolerance in mixed coupling wireless power transfer systems. Full article

Background/Objectives: Traumatic spinal cord (SC) injury (SCI) is a debilitating neurological condition. Minimally invasive approaches to monitor in real time the functional state of the neuromotor apparatus in animal models of SCI (at rest and movement) to assess effectiveness of therapy are needed in preclinical studies. We aimed to develop such a bioethically acceptable platform for SCI studies on non-human primates (Rhesus macaques). Methods: Epidural and myographic electrode implantation (EI) (wireless and wired, connected via a head plug) was performed. After EI, motor responses caused by electrical stimulation of the SC at the level of the cervical and lumbar thickening were recorded; electromyography of the limb muscles was recorded during quadrupedal movement of the animal on a treadmill with simultaneous assessment of movements’ kinematic parameters. Five weeks after EI, three animals underwent lateral hemisection of the SC in the C4–C5 segment under the control of a surgical microscope and intraoperative recording of motor- and sensory-evoked potentials. Results: Within 30 days after SCI, during treadmill testing, a decrease in electromyographic activity of the limb muscles and the volume of angular movement in the joints on the side of the injury was detected. Electrical stimulation at the L2–S1 segments of the SC at a frequency of 30 Hz led to the appearance of a locomotor pattern in the muscles of the hind limbs and an increase in the range of motion. Conclusions: Our platform can be used for pathophysiological studies of various neuromodulation modes and as a basis for the development of control neurointerfaces. Full article

The rapid evolution of wireless communication toward Fifth Generation (5G) networks has enabled unprecedented performance improvement in terms of data rate, latency, reliability, sustainability, and connectivity. Recent years have witnessed an excessive deployment of new 5G networks worldwide. This deployment lead to an exponential growth in traffic flow and a massive number of connected devices requiring a new generation of energy-hungry base stations (BSs). This results in increased power consumption, higher operational costs, and greater environmental impact, making energy efficiency (EE) a critical research challenge. This paper presents a comprehensive survey of EE optimization strategies in 5G networks. It reviews the transition from traditional methods such as resources allocation, energy harvesting, BS sleep modes, and power control to modern artificial intelligence (AI)-driven solutions employing machine learning, deep reinforcement learning, and self-organizing networks (SON). Comparative analyses highlight the trade-offs between energy savings, network performance, and implementation complexity. Finally, the paper outlines key open issues and future directions toward sustainable 5G and beyond-5G (B5G/Sixth Generation (6G)) systems, emphasizing explainable AI, zero-energy communications, and holistic green network design. Full article

Biomedical antennas are essential components in modern healthcare systems, supporting wireless communication, physiological monitoring, diagnostic imaging, and therapeutic energy delivery. Their performance is strongly influenced by proximity to the human body, creating challenges such as impedance detuning, signal absorption, and size constraints that motivate new materials and fabrication approaches. This work reviews recent advances enabling next-generation wearable and implantable antennas, with emphasis on printed electronics, additive manufacturing, flexible hybrid integration, and metamaterial design. Methods discussed include 3D printing and inkjet, aerosol jet, and screen printing for fabricating conductive traces on textiles, elastomers, and biodegradable substrates, as well as multilayer Flexible Hybrid Electronics that co-integrate sensing, power management, and RF components into thin, body-conforming assemblies. Key results highlight how metamaterial and metasurface concepts provide artificial control over dispersion, radiation, and near-field interactions, enabling antenna miniaturization, enhanced gain and focusing, and improved isolation from lossy biological tissue. These approaches reduce SAR, stabilize impedance under deformation, and support more efficient communication and energy transfer. The review concludes that the convergence of novel materials, engineered electromagnetic structures, and AI-assisted optimization is enabling biomedical antennas that are compact, stretchable, personalized, and highly adaptive, supporting future developments in unobtrusive monitoring, wireless implants, point-of-care diagnostics, and continuous clinical interfacing. Full article

Unmanned aerial vehicle (UAV) swarms offer an efficient solution for data collection from widely distributed ground users (GUs). However, incomplete environment information and frequent changes make it challenging for standard centralized planning or pure reinforcement learning approaches to simultaneously maintain global solution quality and local flexibility. We propose a hierarchical data collection framework for heterogeneous UAV-assisted wireless sensor networks (WSNs). A small set of high-capability UAVs (H-UAVs), equipped with substantial computational and communication resources, coordinate regional coverage, trajectory planning, and uplink transmission control for numerous resource-constrained low-capability UAVs (L-UAVs) across power-Voronoi-partitioned areas using multi-agent deep reinforcement learning (MADRL). Specifically, we employ Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enhance H-UAVs’ decision-making capabilities and enable coordinated actions. The partitions are dynamically updated based on GUs’ data generation rates and L-UAV density to balance workload and adapt to environmental dynamics. Concurrently, a large number of L-UAVs with limited onboard resources perform self-organized data collection from GUs and execute opportunistic relaying to a remote access point (RAP) via H-UAVs. Within each Voronoi cell, L-UAV motion follows a weighted Vicsek model that incorporates GUs’ age of information (AoI), link quality, and congestion avoidance. This spatial decomposition combined with decentralized weak-swarm control enables scalability to large-scale L-UAV deployments. Experiments demonstrate that the proposed strong and weak agent MADDPG (SW-MADDPG) scheme reduces AoI by 30% and 21% compared to No-Voronoi and Heuristic-HUAV baselines, respectively. Full article