Search Results (61)

Exercise fatigue is a critical factor that compromises athletic performance, increases the risk of musculoskeletal injury, and threatens safety in military and occupational settings. Reliable monitoring of fatigue is therefore essential for optimizing training, preventing injury, and safeguarding long-term health. Biomechanical indicators, including joint kinematics, ground reaction forces, and electromyographic signals, provide valuable insight into the biomechanical manifestations of fatigue. Although traditional laboratory-based methods are accurate, they are costly, cumbersome, and unsuitable for continuous field monitoring. Recent advances in wearable technologies, particularly inertial measurement units (IMUs), insole pressure sensors (IPSs), and surface electromyography (sEMG), enable continuous, noninvasive, and real-time assessment of biomechanical changes during exercise fatigue. This review synthesizes current progress in IMU-, IPS-, and sEMG-based wearable systems for biomechanical exercise fatigue monitoring, highlighting their principles, strengths, and challenges. Full article

Rear-end collisions involving maintenance vehicles remain a critical source of severe injuries and fatalities in highway work zones. Existing studies on Rear Impact Guards (RIGs) and Truck-Mounted Attenuators (TMAs) have primarily relied on vehicle-based acceleration metrics or low-speed tests, leaving uncertainty regarding their performance under high-energy impact conditions. This study investigates occupant injury risk and vehicle crash behavior through full-scale frontal impact tests conducted at 80 km/h using a 2002 Renault SM520 passenger car against (1) a truck equipped with a RIG and (2) the same truck equipped with a TMA. Hybrid III 50th percentile ATDs, high-speed imaging, and multi-axis accelerometers were employed to measure occupant kinematics and injury responses. Occupant Risk Indices (THIV (Theoretical Head Impact Velocity), ASI (Acceleration Severity Index), PHD (Post-impact Head Deceleration), and ORA (Occupant Ridedown Acceleration)) and the ATD-based HIC36 were evaluated to assess crash severity. The RIG test exhibited severe underride, resulting in an HIC36 value of 1810, far exceeding the FMVSS 208 limit. In contrast, the TMA significantly reduced occupant injury risk, lowering HIC36 by 83.5%, and maintained controlled vehicle deceleration without compartment intrusion. Comparisons between FSM-based indices and ATD-measured injury responses revealed discrepancies in impact timing and occupant motion, highlighting limitations of current evaluation methodologies. The findings demonstrate the necessity of high-speed testing and ATD-based injury assessment for accurately characterizing RIG/TMA performance and provide evidence supporting improvements to roadside safety hardware standards and work-zone protection strategies. Full article

Current upper-limb rehabilitation robots primarily focus on training tasks involving free movements or static interactions with rigid objects. These paradigms lack simulation of complex object manipulation tasks encountered in daily life, thereby limiting the training of patients’ high-level sensorimotor integration capabilities. To address this gap, this study proposes an innovative robotic rehabilitation training system designed for functional occupational therapy. Specifically, the task of transporting a water cup was abstracted into a cup–ball system integrated with a robotic arm. The ball was modeled as a point mass, and kinematic and dynamic analyses of the system were conducted. A visual tracking method was employed to monitor the ball’s motion and calculate its slosh angle. Owing to the impaired fine motor control in stroke patients, a sloshing suppression control strategy integrating exponential filtering, feedforward force compensation, and impedance control was proposed to prevent the ball from spilling. Experiments validated the effectiveness of the proposed method. The results indicated that with full compensation, the oscillation rate of the ball was significantly reduced, and the smoothness of the hand force was markedly improved. This study presents an effective method for addressing dynamic uncertainty in rehabilitation robot training, thus significantly improving the functional relevance of the training. Full article

Granite wet edge finishing is widely adopted to improve surface durability and aesthetics while reducing dust dispersion compared to dry processes. However, even under flooded lubrication, fine particles (FP, 0.5–20 µm) and ultrafine particles (UFP, <100 nm) containing crystalline silica are emitted, posing health risks such as silicosis and pulmonary or cardiovascular diseases. This study investigates particle emissions during CNC edge finishing of black (containing 0% quartz) and white granites (containing 41% quartz) using two industrially relevant profile tools: Half-Beveled (HB) and Ogee (OG). A full factorial design evaluated the effects of granite type, tool geometry, abrasive grit size, spindle speed, and feed rate. Particle concentrations were measured with Aerodynamic and Scanning Mobility Particle Sizers. Results show that spindle speed (N) is the dominant factor, explaining up to 92% of variance in emissions, whereas feed rate (V_f) played a minor role. Tool geometry had a pronounced effect on UFP release: sharp-edged geometries (HB) promoted localized micro-fracturing and higher emissions, while curved geometries (OG) distributed stresses and reduced particle detachment. White granite generated higher mass emissions due to its high quartz content, while black granite exhibited more stable emission behavior. These findings highlight the dual necessity of optimizing cutting kinematics and selecting appropriate tool profiles to balance surface quality and occupational health in granite processing. Full article

Background: Firefighters are at elevated risk of low back pain (LBP), yet predictors, mechanisms, and interventions for LBP in this occupation remain poorly defined. The purpose of this study was to systematically review the literature and synthesize the evidence on functional biomarkers associated with the risk of LBP in firefighters. Methods: PubMed, EMBASE, CINAHL, and PEDro were searched for studies evaluating functional biomarkers in firefighters with or without LBP, including aerobic capacity, anthropometric measures, disability/kinesiophobia, functional work tasks/capacity, imaging/structural/morphological characteristics, kinematics, movement quality/range of motion, muscular fitness, overall physical fitness, physical activity. Empirical evidence statements were generated for each biomarker domain, under Protocol Registration PROSPERO (CRD420251010061). Results: Eighteen studies (n = 32,977) met inclusion criteria and were predominantly cross-sectional (14/18) with fair quality (13/18), which suggests a substantial risk of bias. Higher disability/kinesiophobia and poorer functional work task performance were linked to increased risk of LBP, although causal relationships cannot be determined. Associations for the eight other biomarkers were inconsistent. Two interventional studies demonstrated benefits from trunk-focused exercise. Conclusions: The literature examining functional biomarkers and LBP in firefighters is fragmented, which precludes making robust and broad clinical recommendations for evidence-based implementation. Findings of future research may ultimately lead to approaches to improve the safety and health of firefighters with LBP through patient-centered and tailored programs addressing integrated functional biomarkers across the continuum of prevention, clinical care, and resilience development. Full article

Background: Ultrasound is a non-invasive imaging technique that provides real-time evaluation of anatomical structures. While versatile in examining various organs, it can be physically demanding for physicians due to the need for challenging positions, causing musculoskeletal pain and potentially work-related diseases over time. The study aimed to assess the ergonomics of abdominal and breast ultrasound, identify the most challenging anatomical area, determine which part of the examination causes the greatest strain, and evaluate the overall ergonomic impact of the entire procedure. Methods: This single-center study involved 4 radiologists and focused on breast and abdominal ultrasonography. Kinematic data were recorded using the Noraxon Ultium Motion inertial system to track body movements during the ultrasound procedures. Five critical segments were identified while examining the liver, right kidney, left kidney, right breast, and left breast. Ergonomic assessment was performed using the Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA) methods, evaluating postural risks and physical strain during each segment and the whole procedure. Results: Both RULA and REBA assessments yielded median total scores of 6.0–7.0 and 6.0–7.5, respectively, reflecting consistently medium to high musculoskeletal loading. Examinations of the left breast and left kidney were associated with the most demanding postures. These elevated scores demonstrate that abdominal and breast ultrasonography imposes substantial ergonomic strain, potentially increasing the risk of work-related musculoskeletal disorders. Conclusions: The high ergonomic risk scores indicate an urgent need to modify scanning techniques and workstation design to reduce musculoskeletal strain in sonographers. Implementing ergonomic improvements is essential to prevent occupational injuries and promote long-term health. Full article

Accurate walking trajectory estimation is critical for monitoring activity levels in healthcare and occupational safety applications. Ultra-Wideband (UWB) technology has emerged as a key solution for indoor human activity and trajectory tracking. However, its performance is fundamentally limited by Non-Line-of-Sight (NLOS) errors and kinematic drift during turns. To address these challenges, this study introduces a novel integrated IMU-UWB framework for walking trajectory estimation in NLOS scenarios involving turning gait. The algorithm integrates an error-state Kalman filter (ESKF) and a phase-aware turning correction module. Experiments were carried out to evaluate the effectiveness of this framework. The results show that the presented framework demonstrates significant improvements in walking trajectory estimation, with a smaller mean absolute error (7.0 cm) and a higher correlation coefficient, compared to the traditional methods. By effectively mitigating both NLOS-induced ranging errors and turn-related drift, this system enables reliable indoor tracking for healthcare monitoring, industrial safety, and consumer navigation applications. Full article

This study presents the results of preliminary documentation and radon tracer investigations conducted at the “Aurelia” Mine in Złotoryja. Measurements of ²²²Rn activity concentrations were carried out between 17 March and 26 August 2023, while terrestrial laser scanning (TLS) for mapping purposes was performed on 16 November 2024. The radon data exhibited a consistently right-skewed distribution, with skewness coefficients ranging from 0.9 to 8.2 and substantial standard deviations, indicating significant data dispersion. Outliers and extreme outliers were identified as key factors influencing average radon activity concentrations from April through August, whereas data from March displayed homogeneity, with no detected anomalies. The average ²²²Rn activity concentrations recorded from March to July ranged from 51.4 Bq/m³ to 65.9 Bq/m³. In contrast, July and August showed elevated average values (75.8 Bq/m³ and 5784.8 Bq/m³, respectively) due to the presence of outliers and extreme values. Upon removal of these anomalies, the adjusted means were 73.8 Bq/m³ and 1003.6 Bq/m³, respectively, resulting in reduced skewness and improved representativeness. These findings suggest that the annual average radon concentrations at the “Aurelia” Mine remain compliant with the regulatory threshold of 300 Bq/m³ set by the Atomic Law Act, with exceedances likely related to atypical or rare geophysical phenomena requiring further statistical validation. August exhibited a significant occurrence of outliers and extreme outliers in ²²²Rn activity concentration data, particularly concentrated between the 13th and 17th days of the month. This anomaly is hypothesized to be associated with geological processes, notably mining-induced seismic events within the LGOM (Legnica–Głogów Copper District) region. It is proposed that periodic transitions between tensional and compressional phases within the rock mass, triggered by mining activity, may lead to abrupt increases in radon exhalation, potentially occurring before or after seismic events with a magnitude exceeding 2.5. Although the presented data provide preliminary evidence supporting the influence of tectonic kinematic changes on subsurface radon dynamics, further systematic observations are required to confirm this relationship. At the current stage, the hypothesis remains speculative but may contribute to the broader understanding of radon behavior in geologically active underground environments. Complementing the geochemical analysis, TLS enabled detailed geological mapping and 3D spatial modeling of the mine’s underground tourist infrastructure. The resulting simplified linked data model—integrating radon activity concentrations, geological structures, and spatial parameters—provides a foundational framework for developing a comprehensive GIS database. This integrative approach highlights the feasibility of combining tracer studies with spatial and cartographic data to improve radon risk assessment models and ensure regulatory compliance in underground occupational settings. Full article

Rear-end collisions represent a major concern in automotive safety, particularly due to the risk of whiplash injuries among vehicle occupants. The accurate simulation of occupant kinematics during such impacts is critical for the development of advanced safety systems. This paper presents an enhanced multibody simulation model specifically designed for rear-end crash scenarios, incorporating integrated active headrest mechanisms and sensor-based activation logic. The model combines detailed representations of vehicle structures, suspension systems, restraint systems, and occupant biomechanics, allowing for the precise prediction of crash dynamics and occupant responses. The system was developed using Simscape Multibody, with CAD-derived components interconnected through physical joints and validated using controlled experimental crash tests. Special attention was given to modelling contact forces, suspension behaviour, and actuator response times for the active headrest system. The model achieved a root mean square error (RMSE) of 4.19 m/s² and a mean absolute percentage error (MAPE) of 0.71% when comparing head acceleration in frontal collision tests, confirming its high accuracy. Validation results demonstrate that the model accurately reproduces occupant kinematics and head acceleration profiles, confirming its reliability and effectiveness as a predictive tool. This research highlights the critical role of integrated sensor-actuator systems in improving occupant safety and provides a flexible platform for future studies on intelligent vehicle safety technologies. Full article

Piano-based occupational therapy has emerged as an engaging and effective rehabilitation strategy for improving upper limb motor functions. However, a lack of comprehensive biomechanical modeling, objective rehabilitation assessment, and real-time fatigue monitoring has limited its clinical optimization. This study developed a comprehensive “key–finger–exoskeleton” biomechanical model based on Hill-type muscle dynamics and rigid-body kinematics. A three-dimensional muscle synergy analysis method using non-negative tensor factorization (NTF) was proposed to quantitatively assess rehabilitation effectiveness. Furthermore, a real-time Comprehensive Muscle Fatigue Index (CMFI) based on multi-muscle coordination was designed for fatigue monitoring during therapy. Experimental validations demonstrated that the biomechanical model accurately predicted interaction forces during piano-playing tasks. After three weeks of therapy, patients exhibited increased synergy modes and significantly improved similarities with healthy subjects across spatial, temporal, and frequency domains, particularly in the temporal domain. The CMFI showed strong correlation (r > 0.83, p < 0.001) with subjective fatigue ratings, confirming its effectiveness in real-time fatigue assessment and training adjustment. The integration of biomechanical modeling, synergy-based rehabilitation evaluation, and real-time fatigue monitoring offers an objective, quantitative framework for optimizing piano-based rehabilitation. These findings provide important foundations for developing intelligent, adaptive rehabilitation systems. Full article

This article presents the design, implementation, and validation of a Remotely Piloted Safety Cone (RPSC), an autonomous robotic system developed to enhance safety and operational efficiency in road maintenance. The RPSC addresses challenges associated with road works, including workers’ exposure to traffic hazards and inefficiencies of traditional traffic cones, such as manual placement and retrieval, limited visibility in low-light conditions, and inability to adapt to dynamic changes in work zones. In contrast, the RPSC offers autonomous mobility, advanced visual signalling, and real-time communication capabilities, significantly improving safety and operational flexibility during maintenance tasks. The RPSC integrates sensor fusion, combining Global Navigation Satellite System (GNSS) with Real-Time Kinematic (RTK) for precise positioning, Inertial Measurement Unit (IMU) and encoders for accurate odometry, and obstacle detection sensors within an optimised navigation framework using Robot Operating System (ROS2) and Micro Air Vehicle Link (MAVLink) protocols. Complying with European regulations, the RPSC ensures structural integrity, visibility, stability, and regulatory compliance. Safety features include emergency stop capabilities, visual alarms, autonomous safety routines, and edge computing for rapid responsiveness. Field tests validated positioning accuracy below 30 cm, route deviations under 15 cm, and obstacle detection up to 4 m, significantly improved by Kalman filtering, aligning with digitalisation, sustainability, and occupational risk prevention objectives. Full article

This study investigates occupant kinematic and injury responses in zero-gravity seats under rear impacts at 16 km/h, 40 km/h, and 56 km/h and evaluates the protective performance of a conventional three-point seat belt system and a four-point seat belt system. First, a THUMS (Total Human Model for Safety)-based finite element assembly consisting of a regular seat model and a conventional three-point seat belt system was verified by comparing the kinematic responses and time-history curves of head acceleration, head rotation, and the T1 acceleration of PMHS (Postmortem Human Subject) tests. Then, a THUMS-based finite element assembly in a zero-gravity seat with a three-point seat belt system was created, and computational biomechanical analyses revealed that at low-to-medium impact speeds (16 and 40 km/h), the occupant exhibited backward sliding in the zero-gravity seat along the seatback with lower limb rotation and did not experience head and neck injury. However, a 56 km/h impact induced an excessive seatback rotation and caused the head to become out of position. The neck collided with the upper part of the headrest and caused a surge in the contact force between the neck and the headrest. The head injury and neck injury were comprehensively analyzed via the head injury metrics and neck injury metrics, including cervical spine injury metrics and cervical ligament injury metrics. Further, a four-point seat belt system was adopted and demonstrated better and more balanced restraining effects by reducing the relative displacement between the occupant’s head and chest in the x- and y-directions by 26% and 84%, respectively. Therefore, the occupant’s head remains in position and the collision between the neck and the headrest can be avoided. Maximum reductions in the head and neck injury metrics reached 70% and 57%, respectively. The current study illustrates the disadvantages of the traditional three-point seat belt system in restraining the occupant in a zero-gravity seat under rear impact and shows the four-point seat belt to be a better alternative. This study sheds light on seat belt system design and optimization towards future zero-gravity seats under rear impact. Full article

The reliable and uncertainty-aware prediction of surrounding vehicles remains a key challenge in autonomous driving. However, existing methods often struggle to quantify and incorporate uncertainty effectively. To address these challenges, we propose a hybrid architecture that combines a data-driven neural trajectory predictor with physically grounded constraints to forecast future vehicle occupancy. Specifically, the physical constraints are derived from vehicle kinematic principles and embedded into the network as additional loss terms during training. This integration ensures that predicted trajectories conform to feasible and physically realistic motion boundaries. Furthermore, a mixture density network (MDN) is employed to estimate predictive uncertainty, transforming deterministic trajectory predictions into spatial probability distributions. This enables a probabilistic occupancy representation, offering a richer and more informative description of the potential future positions of surrounding vehicles. The proposed model is trained and evaluated on the Aerial Dataset for China’s Congested Highways and Expressways (AD4CHE), which contains representative driving scenarios in China. Experimental results demonstrate that the model achieves strong fitting performance while maintaining high physical plausibility in its predictions. Full article

In this study, the static and kinematic positioning performance of the Garmin GPSMAP 66sr handheld GNSS receiver has been tested. For the static test, GNSS data was collected for 24 h and divided into shorter sessions of 1, 2, and 4 h to assess the performance of the receiver as a function of occupation time. The whole and subgroup data were processed by the relative method for different satellite constellations using three reference stations, to form a very short (45 m), short (5.1 km), and relatively long (73.2 km) baselines. For the kinematic test, the data was collected for approximately 1 h and processed with the relative method. Additionally, the whole and subgroup static and kinematic GNSS data of the Garmin receiver were also processed with the Canadian Spatial Reference System-Precise Point Positioning (CSRS-PPP) online service. All Garmin static and kinematic solutions (both relative and PPP) were compared with those calculated by the geodetic receiver. The overall static results show that the Garmin GPSMAP 66sr handheld receiver provides accuracy in a few centimeters with the relative method when integer ambiguities were correctly fixed and in the decimeter-to-meter level using the PPP technique. For the kinematic scenario, the results were relatively poor within the level of decimeters with the relative method while the level of meters with the PPP technique. Full article

This manuscript presents an updated review of back exoskeletons for occupational use, with a particular focus on sensor technology as a key enabler for intelligent and adaptive support. The study aims to identify key barriers to adoption and explore design characteristics which align these systems with the Industry 5.0 paradigm, where machines function as collaborative co-pilots alongside humans. We propose a structured design pipeline and analyze 32 exoskeletons across multiple dimensions, including design, actuation, control strategies, sensor networks, and intelligence. Additionally, we review eight simulation environments which support the early stages of exoskeleton development. Special emphasis is placed on sensor technology, highlighting its critical role in enhancing adaptability and intelligence. Our findings reveal that while 39.39% of exoskeletons accommodate asymmetric activities, kinematic compatibility remains a challenge. Furthermore, only 33.33% of the systems incorporated intelligent features, with just one being capable of adapting its response based on poor posture or real-time human–machine interaction feedback. The limited integration of advanced sensors and decision-making capabilities constrains their potential for dynamic and adaptive support. Open questions remain in high-level decision making, enhanced environmental awareness, and the development of generalizable methods for integrating sensor data into adaptive control strategies. Full article