Search Results (552)

In recent years, various devices utilizing surface acoustic waves (SAW) have emerged as powerful tools for manipulating particles and fluids in microchannels. Although they demonstrate a wide range of functionalities across diverse applications, existing devices still face limitations in flexibility, manipulation efficiency, and spatial resolution. In this study, we developed a dual-sided standing surface acoustic wave (SSAW) device that simultaneously excites acoustic waves through two piezoelectric substrates positioned at the top and bottom of a microchannel. By fully exploiting the degrees of freedom offered by two pairs of interdigital transducers (IDTs) on each substrate, the system enables highly flexible control of microparticles. To explore its capability on particle aggregation, we developed a two-dimensional numerical model to investigate the influence of the SAW phase modulation on the established acoustic fields within the microchannel. Single-particle motion was first examined under the influence of the phase-modulated acoustic fields to form a reference for identifying effective phase modulation strategies. Key parameters, such as the phase changes and the duration of each phase modulation step, were determined to maximize the lateral motion while minimizing undesired vertical motion of the particle. Our dual-sided SSAW configuration, combined with novel dynamic phase modulation strategy, leads to rapid and precise aggregation of microparticles towards a single focal point. This study sheds new light on the design of acoustofluidic devices for efficient spatiotemporal particle concentration. Full article

Electroencephalography (EEG) is the only brain imaging method light enough and with the temporal precision to assess electrocortical dynamics during human locomotion. However, head motion during whole-body movements produces artifacts that contaminate the EEG and reduces ICA decomposition quality. We compared commonly used motion artifact removal approaches for reducing the motion artifact from the EEG during running and identifying stimulus-locked ERP components during an adapted flanker task. EEG was recorded from young adults during dynamic jogging and static standing versions of the Flanker task. Motion artifact removal approaches were evaluated based on their ICA’s component dipolarity, power changes at the gait frequency and harmonics, and ability to capture the expected P300 ERP congruency effect. Preprocessing the EEG using either iCanClean with pseudo-reference noise signals or artifact subspace reconstruction (ASR) led to the recovery of more dipolar brain independent components. In our analyses, iCanClean was somewhat more effective than ASR. Power was significantly reduced at the gait frequency after preprocessing with ASR and iCanClean. Finally, preprocessing using ASR and iCanClean also produced ERP components similar in latency to those identified in the standing flanker task. The expected greater P300 amplitude to incongruent flankers was identified when preprocessing using iCanClean. ASR and iCanClean may provide effective preprocessing methods for reducing motion artifacts in human locomotion studies during running. Full article

Background: Aquatic therapy (AT), immersion in hot water, and supported standing are frequently used to manage spasticity, contractures, and joint retractions in children with cerebral palsy (CP). Recently, the use of exoskeletons has been offering a new treatment option for severe CP. This study aimed to compare the post-immediate effects of four treatments on spasticity, range of motion, and the heart rate of children with severe CP. Methods: Three children with spastic CP (levels IV and V GMFCS) received a single 30-min session in consecutive weeks of robot-assisted gait training (RAGT), AT, supported standing, and immersion in hot water. Post-immediate assessments included knee flexor spasticity (modified Ashworth scale, MAS, and modified Tardieu scale, MTS); knee range of motion (ROM, in degrees (°)); and heart rate (HR). Results: AT and supported standing induced greater reductions in spasticity based on MAS scores. RAGT demonstrated superior spasticity reduction using MTS and yielded the greatest improvement in popliteal angle (mean increase: 27°). AT and RAGT induced a 14 beats-per-minute change in HR, indicating moderate cardiovascular engagement. Conclusions: RAGT appears particularly effective in improving spasticity and ROM in children with severe CP. Nonetheless, conventional treatments still offer an effective option when addressing spasticity. Full article

Mangrove forests are vital blue carbon (BC) ecosystems that significantly contribute to climate change mitigation through carbon sequestration. Accurate, scalable, and cost-effective methods for estimating carbon stocks in these environments are essential for conservation planning. In this study, we assessed the potential of drones, also known as unmanned aerial vehicles (UAVs), for estimating above-ground biomass (AGB) and BC in Avicennia marina stands by integrating drone-based canopy measurements with field-measured tree heights. Using structure-from-motion (SfM) photogrammetry and a consumer-grade drone, we generated a canopy height model and extracted structural parameters from individual trees in the Melgonze mangrove patch, southern Iran. Field-measured tree heights served to validate drone-derived estimates and calibrate an allometric model tailored for A. marina. While drone-based heights differed significantly from field measurements (p < 0.001), the resulting AGB and BC estimates showed no significant difference (p > 0.05), demonstrating that crown area (CA) and model formulation effectively compensate for height inaccuracies. This study confirms that drones can provide reliable estimates of BC through non-invasive means—eliminating the need to harvest, cut, or physically disturb individual trees—supporting their application in mangrove monitoring and ecosystem service assessments, even under challenging field conditions. Full article

(1) Background: A severe decline in knee joint function significantly affects the mobility of the elderly, making it a key concern in the field of geriatric health. To alleviate the pressure on the knee joints of the elderly during daily movements such as sitting and standing, effective biomechanical solutions are required. (2) Methods: In this study, a biomechanical framework was established based on mechanical analysis to derive the transfer relationship between the ground reaction force and the knee joint moment. Experiments were designed to collect knee joint data on the elderly during the sit-to-stand process. Meanwhile, magnetic resonance imaging (MRI) images were processed through a medical imaging control system to construct a detailed digital 3D knee joint model. A finite element analysis was used to verify the model to ensure the accuracy of its structure and mechanical properties. An improved radial basis function was used to fit the pressure during the entire sit-to-stand conversion process to reduce the computational workload, with an error of less than 5%. In addition, a small-target human key point recognition network was developed to analyze the image sequences captured by the camera. The knee joint angle and the knee joint pressure distribution during the sit-to-stand conversion process were mapped to a three-dimensional interactive platform to form a digital twin system. (3) Results: The system can effectively capture the biomechanical behavior of the knee joint during movement and shows high accuracy in joint angle tracking and structure simulation. (4) Conclusions: This study provides an accurate and comprehensive method for analyzing the biomechanical characteristics of the knee joint during the movement of the elderly, laying a solid foundation for clinical rehabilitation research and the design of assistive devices in the field of rehabilitation medicine. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

Wireless structural health monitoring is needed for machine elements of which the working motions prevent wired monitoring. Rotating machine shafts are such elements. Wired monitoring of the rotating shaft requires making significant changes to the shaft structure, primarily drilling a hole in the longitudinal axis of the shaft and installing a slip ring assembly at the end of the shaft. Such changes to the shaft structure are not always possible. This paper proposes the use of piezoelectric energy harvesting from a rotating shaft to power wireless temperature monitoring of the shaft surface. The main components of presented wireless temperature monitoring are three piezoelectric composite patches, three thermal fuses, a system for storing and distributing the harvested energy, and a radio transmitter. This article contains the results of experimental research of such wireless monitoring on a dedicated laboratory stand. This research included four connections of piezoelectric composite patches: delta, star, parallel, and series for different capacities of a storage capacitor. Based on experimental results, three parameters that influence the frequency of sending data packets by the presented wireless temperature monitoring are identified: amplitude of stress in the rotating shaft, rotation speed of the shaft, and the capacity of a storage capacitor. Full article

Background/Objectives: In recent years, scientific literature has illustrated the growing interest in telerehabilitation after ACL reconstruction. The aim of this study is to compare the effectiveness of remotely supervised rehabilitation with traditional supervised rehabilitation after ACLR, focusing on objective postoperative functional assessment outcomes. Methods: A retrospective analysis of prospectively collected data was carried out, selecting patients that underwent arthroscopic ACLR by a single surgeon. Functional assessments of the patients were carried out at 1 and 2 weeks and 1, 2 and 3 months after surgery, including range of motion (ROM), maximal voluntary isometric contractions (MVICs) of extensor and flexor muscles, the sit-to-stand test and the countermovement jump. Intergroup statistics were carried out using a non-inferiority hypothesis. Results: A total of 251 patients were included in this study (supervised rehabilitation n = 165; remotely supervised rehabilitation n = 86). Functional assessment improved over time in both groups. The extension ROM deficit decreased to 0 difference 30 days after surgery. The median flexion ROM ILD at 60 days was significantly different among the groups, with a residual 10° ILD in the Group R compared with 0° ILD in group S (p = 0.01). All other assessments did not achieve statistical significance. Conclusions: The results support the integration of a digital rehabilitation tool in post-ACLR recovery programs. The results suggest that remotely supervised rehabilitation can be a viable alternative to traditional supervised rehabilitation for early-stage recovery. However, more research is needed to optimize protocols and to identify patients who may benefit most from this approach. Full article

This study evaluates the accuracy of a low-cost wearable system for the continuous monitoring of vital signs, including heart rate, blood oxygen saturation (Sp${\mathrm{O}}_2$), blood pressure trend (BPT), and body temperature. The prototype was built using the nRF52840 microcontroller, which integrates photoplethysmography and infrared sensors. The heart rate and Sp${\mathrm{O}}_2$ data were collected under three body positions (Rest, Sitting, and Standing), while all measurements were performed using both anatomical configurations: BPT-Finger and BPT-Earlobe. Results were compared against validated commercial devices: UT-100 for heart rate and Sp${\mathrm{O}}_2$, G-TECH LA800 for blood pressure, and G-TECH THGTSC3 for body temperature. Ten participants were monitored over a ten-day period. Bland–Altman analysis revealed clinically acceptable agreement thresholds of ±5 mmHg for blood pressure, ±5–10 bpm for heart rate, ±4% for Sp${\mathrm{O}}_2$, and ±0.5 °C for temperature. Both wearable configurations demonstrated clinically acceptable agreement across all vital signs. The BPT-Earlobe configuration exhibited superior stability and lower variability in the Rest and Sitting positions, likely due to reduced motion artifacts. Conversely, the BPT-Finger configuration showed higher Sp${\mathrm{O}}_2$ accuracy in the Standing position, with narrower limits of agreement. These findings highlight the importance of sensor placement in maintaining measurement consistency across physiological conditions. With an estimated cost of only ~USD 130—compared to ~USD 590 for the commercial alternatives—the proposed system presents a cost-effective, scalable, and accessible solution for decentralized health monitoring, particularly in underserved or remote environments. Full article

Background/Objectives: Spasticity in children with cerebral palsy (CP) can impair motor-related functions. The objective of this exploratory, prospective study was to examine if transcutaneous electrical nerve stimulation (TENS) in a whole-body suit leads to changes in spasticity and other related effects. Methods: Thirty-one children with CP GMFCS III–V, with a median age of 11.0 years (age range of 7–17 years), were consecutively included, and they used the suit with TENS for 24 weeks. The primary outcome was spasticity measured using the Modified Ashworth Scale (MAS). Functional motor-related tasks were evaluated by the Goal Attainment Scale (SMART GAS). The Modified Tardieu Scale (MTS), passive Range of Motion (pROM), GMFM-66, and Posture and Postural Ability Scale (PPAS) assessments were performed. Results: Seventeen subjects (17/31) completed the 24 weeks. Dropout was due to difficulty in donning the suit. The level of overall spasticity, most pronounced in the proximal arms and legs, was reduced according to the MAS, but not the MTS or pROM. Subject-relevant motor-related goals improved significantly in standing/walking and hand/arm function. Changes in the GMFM-66 and PPAS were not significant. Conclusions: Although there were statistically significant but underpowered changes in the MAS after 24 weeks, there were no clinically relevant effects. Exploratorily, we found observer-reliant motor-related functional improvements, which, however, we were unable to detect when trying to quantify them. Donning the suit led to dropout throughout the study. Caregivers need to allocate time, mental capacity and have the physical skill set for donning the suit for long-term use. Full article

Object-related accidents among older adults often result from inadequately designed furniture and fixtures that do not accommodate age-related changes. However, technologies for quantitatively capturing how furniture and fixtures assist the body in daily life remain limited. This study addresses this gap by introducing a portable, non-disruptive system that measures and visualizes how humans interact with environmental objects, particularly during transitional movements such as standing, turning, or reaching. The system integrates wearable force sensors, motion capture gloves, RGB-D cameras, and LiDAR-based environmental scanning to generate spatial maps of body-applied forces, overlaid onto point cloud representations of actual living environments. Through home-based experiments involving 13 older adults aged 69–86 across nine households, the system effectively identified object-specific support interactions with specific furniture (e.g., doorframes, shelves) and enabled a three-dimensional comparative analysis across different spaces, including living rooms, entryways, and bedrooms. The visualization captured essential spatial features—such as contact height and positional context—without altering the existing environment. This study presents a novel methodology for evaluating life environments from a life-centric perspective and offers insights for the inclusive design of everyday objects and spaces to support safe and independent aging in place. Full article

This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human–machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% ± 1.4% accuracy, 68.7 ms ± 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (κ = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km/h), while distributed learning across multiple devices maintains consistent accuracy (variance < 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems. Full article

Background/Objectives: The assessment of relationships between trunk muscle activity and thoraco-lumbar movements during sagittal bending has demonstrated that low back pain (LBP) subgroups (flexion pattern and active extension pattern motor control impairment) reveal distinct relationships that differentiate these subgroups from control groups. The study objective was to establish whether such relationships exist during various daily activities. Methods: Fifty participants with non-specific chronic low back pain (NSCLBP) (27 flexion pattern (FP), 23 active extension pattern (AEP)) and 28 healthy controls were recruited. Spinal kinematics were analysed using 3D motion analysis (Vicon™, Oxford, UK) and the muscle activity recorded via surface electromyography during a range of activities (box lift, box replace, reach up, step up, step down, stand-to-sit, and sit-to-stand). The mean sagittal angles for upper and lower thoracic and lumbar regions were correlated with normalised mean amplitude electromyography of bilateral transversus abdominis/internal oblique (IO), external oblique (EO), superficial lumbar multifidus (LM), and erector spinae (ES). Relationships were assessed via Pearson correlations (significance p < 0.01). Results: In the AEP group, increased spinal extension was associated with altered LM activity during box-replace, reach-up, step-up, and step-down tasks. In the FP group, increased lower lumbar spinal flexion was associated with reduced muscle activation, while increased lower thoracic flexion was associated with increased muscle activation. The control group elicited no significant associations. Correlations ranged between −0.812 and 0.754. Conclusions: Differential relationships between muscle activity and spinal kinematics exist in AEP, FP, and pain-free control groups, reinforcing previous observations that flexion or extension-related LBP involves distinct motor control strategies during different activities. These insights could inform targeted intervention approaches, such as movement-based interventions and wearable technologies, for these groups. Full article

This study investigated the efficacy of Augmented Reality (AR) in the rehabilitation of lower limb amputees by comparing movement accuracy and subjective satisfaction with and without AR. Ten individuals with unilateral lower limb amputations participated. Joint range of motion during exercises was measured using a 3D motion capture system. Subjective satisfaction was assessed via a 5-point Likert scale. Movement accuracy was evaluated by analyzing changes and variability (standard deviation) in joint angles across eight selected movements. Results showed that AR feedback significantly increased average joint angles in specific movements: standing arm raise to the side, standing trunk lateral flexion, and standing knee flexion to maximum height. Furthermore, AR feedback led to a significant reduction in the standard deviation of joint angles for most exercises, indicating improved movement consistency. Subjective satisfaction scores for interest, motivation, exercise effectiveness, movement accuracy, and overall satisfaction were significantly higher with AR. Full article

Video surveillance is widely used in different areas like roads, malls, education, industries, retail, parks, bus stands, and restaurants, each presenting distinct anomaly patterns that demand specialized detection strategies. Adapting anomaly detection models to new camera viewpoints or environmental variations within the same scenario remains a significant challenge. Extending these models to entirely different surveillance environments or scenarios often requires extensive retraining, which can be both resource-intensive and time-consuming. To overcome these limitations, model frameworks, i.e., the video anomaly detector model, have been proposed, leveraging the meta-learning framework for faster adaptation using swin transformer for feature extraction to new concepts. In response, the dataset named MSAD (multi-scenario anomaly detection) having 14 different scenarios from multiple camera views, is the high resolution anomaly detection dataset that includes diverse motion patterns and challenging variations such as varying lighting and weather conditions, offering a robust foundation for training advanced anomaly detection models. Experiments validate the effectiveness of the proposed framework, which integrates model-agnostic meta-learning (MAML) with a ten-shot, one-query adaptation strategy. Leveraging the swin transformer as a spatial feature extractor, the model captures rich hierarchical representations from surveillance videos. This combination enables rapid generalization to novel viewpoints within the same scenario and maintains competitive performance when deployed in entirely new environments. These results highlight the strength of MAML in few-shot learning settings and demonstrate its potential for scalable anomaly detection across diverse surveillance scenarios. Full article