Search Results (2,226)

The localization of Unmanned Aerial Vehicles (UAVs) is a critical area of research, particularly in applications requiring high accuracy and reliability in Global Positioning System (GPS)-denied environments. This paper presents a comprehensive overview of short-distance localization methods for UAVs, exploring their strengths, limitations, and practical applications. Among short-distance localization methods, ultra-wideband (UWB) technology has gained significant attention due to its ability to provide accurate positioning, resistance to multipath interference, and low power consumption. Different approaches to the usage of UWB sensors, such as time of arrival (ToA), time difference of arrival (TDoA), and double-sided two-way ranging (DS-TWR), alongside their integration with complementary sensors like Inertial Measurement Units (IMUs), cameras, and visual odometry systems, are explored. Furthermore, this paper provides an evaluation of the key factors affecting UWB-based localization performance, including anchor placement, synchronization, and the challenges of combined use with other localization technologies. By highlighting the current trends in UWB-related research, including its increasing use in swarm control, indoor navigation, and autonomous landing, potential researchers could benefit from this study by using it as a guide for choosing the appropriate localization techniques, emphasizing UWB technology’s potential as a foundational technology in advanced UAV applications. Full article

Inertial measurement units (IMUs) are an example of practical technology for measuring countermovement jump (CMJ) performance, but there is a need to enhance their validity. One potential strategy to achieve this is advancing the literature on IMU placement. Many studies opt to position a single IMU on anatomical landmarks rather than determining placement based on anthropometric principles, despite the knowledge that linear mechanics act through the segmental centers of mass of the human body. The purpose of this study was to evaluate the impact of positioning IMU sensors to approximate the trunk and lower-extremity segmental centers of mass on the validity of vertical acceleration measurements and jump height (JH) estimation during CMJs. Thirty young adults (female n = 10, 21.3 (3.8) years, 166.1 (4.1) cm, 67.6 (11.3) kg; male n = 20, 22.0 (2.6) years, 179.2 (6.4) cm, 83.5 (17.1) kg) from a university setting participated in the study. Seven IMUs were positioned at the approximate centers of mass of the trunk, thighs, shanks, and feet. Using data from these sensors, 15 whole-body center of mass models were developed, including 1-, 2-, 3-, and 4-segment configurations derived from the trunk and three lower-body segments. The root mean square error (RMSE) of vertical acceleration was calculated for each IMU model by comparing its data against vertical acceleration measurements obtained from a force platform. JH estimates were calculated using the take-off velocity method and compared across IMU models and the force platform to evaluate for systematic bias. RMSE and JH values from the best-performing 1-, 2-, 3-, and 4-segment IMU models were analyzed for main effects using one-way analyses of variance. The best performing 2-segment (trunk and shanks; RMSE = 2.1 ± 1.3 m × s⁻²) and 3-segment (trunk, thighs, and feet; RMSE = 2.0 ± 1.2 m × s⁻²) IMU models returned significantly lower RMSE values compared to the 1- segment (trunk; RMSE = 3.0 ± 1.4 m × s⁻²) model (p = 0.021–0.041). No systematic bias was detected between the JH estimates derived from the best-performing IMU models and those obtained from the force platform (p = 0.91–0.99). Positioning multiple IMU sensors to approximate segmental centers of mass significantly improved the validity of vertical acceleration time-series data from CMJs. The findings highlight the importance of anthropometric-based IMU placement for enhancing measurement accuracy without introducing systematic bias. Full article

Promoting alternative modes of transportation such as cycling represents a valuable strategy to minimize environmental impacts, as confirmed in the main targets set out by the European Commission. In this regard, in cities throughout the world, there has been a significant increase in the construction of bicycle paths in recent years, requiring effective maintenance strategies to preserve their service levels. The continuous monitoring of road networks is required to ensure the timely scheduling of optimal maintenance activities. This involves regular inspections of the road surface, but there are currently no automated systems for monitoring cycle paths. In this study, an integrated monitoring and assessment system for cycle paths was developed exploiting Raspberry Pi technologies. In more detail, a low-cost Inertial Measurement Unit (IMU), a Global Positioning System (GPS) module, a magnetic Hall Effect sensor, a camera module, and an ultrasonic distance sensor were connected to a Raspberry Pi 4 Model B. The novel system was mounted on a e-bike as a test vehicle to monitor the road conditions of various sections of cycle paths in Rome, characterized by different pavement types and decay levels as detected using the whole-body vibration $a_{wz}$ index (ISO 2631 standard). Repeated testing confirmed the system’s reliability by assigning the same vibration comfort class in 74% of the cases and an adjacent one in 26%, with an average difference of 0.25 m/s², underscoring its stability and reproducibility. Data post-processing was also focused on integrating user comfort perception with image data, and it revealed anomaly detections represented by numerical acceleration spikes. Additionally, data positioning was successfully implemented. Finally, $a_{wz}$ measurements with GPS coordinates and images were incorporated into a Geographic Information System (GIS) to develop a database that supports the efficient and comprehensive management of surface conditions. The proposed system can be considered as a valuable tool to assess the pavement conditions of cycle paths in order to implement preventive maintenance strategies within budget constraints. Full article

Background/Objectives: This study compared step-by-step kinematic measurements from an infrared contact mat (IR-mat) and an inertial measurement unit (IMU) system during bounding and single leg jumping for speed, while also evaluating the validity of algorithms originally developed for sprinting and running when applied to horizontal jumps. The aim was to investigate differences in contact times between the systems. Methods: Nineteen female football players (15 ± 0.5 years, 61.0 ± 5.9 kg, 1.70 ± 0.06 m) performed attempts in both jumps over 20 m with maximum speed, of which the first eight steps were analysed. Results: Significant differences were found between the systems, with the IR-mat recording longer contact times than the IMU. The IR-mat began and ended its measurements slightly earlier and later, respectively, compared to the IMU system, likely due to the IMU’s algorithm, which was developed for sprinting with forefoot contact, while more midfoot and heel landing is used during jumps. Conclusions: Both systems provide reliable measurements; however, the IR mat consistently records slightly longer contact times for horizontal jumps. While the IMU is dependable, it exhibits a consistent bias compared to the IR mat. For bounding, the IR mat begins recording 0.018 s earlier at touch down and stops 0.021 s later. For single leg jumps, it starts 0.024 s earlier and ends 0.021 s later, resulting in contact times that are, on average, 0.039–0.045 s longer. These findings provide valuable insights for coaches and researchers in selecting appropriate measurement tools, highlighting the systematic differences between IR mats and IMUs in horizontal jump analysis. Full article

The purpose of this study was to retrospectively and prospectively explore associations between running biomechanics and hamstring strain injury (HSI) using field-based technology. Twenty-three amateur sprinters performed 40 m maximum-effort sprints and then underwent a one-year injury surveillance period. For the first 30 m of acceleration, sprint mechanics were quantified through force–velocity profiling. In the upright phase of the sprint, an inertial measurement unit (IMU) system measured sagittal plane pelvic and hip kinematics at the point of contact (POC), as well as step and stride time. Cross-sectional analysis revealed no differences between participants with a history of HSI and controls except for anterior pelvic tilt (increased pelvic tilt on the injured side compared to controls). Prospectively, two participants sustained HSIs in the surveillance period; thus, the small sample size limited formal statistical analysis. A review of cohort percentiles, however, revealed both participants scored in the higher percentiles for variables associated with a velocity-oriented profile. Overall, this study may be considered a feasibility trial of novel technology, and the preliminary findings present a case for further investigation. Several practical insights are offered to direct future research to ultimately inform HSI prevention strategies. Full article

Traditional assessments of balance and postural control often face challenges related to accessibility, cost, subjectivity, and inter-rater reliability. With advancements in technology, smartphones equipped with inertial measurement units (IMUs) are emerging as a promising tool for assessing postural control, measuring both static and dynamic motion. This study aimed to develop and validate a novel smartphone application by comparing it with research-grade posturography instruments, including motion capture and force plate systems to establish construct- and criterion-related validity. Twenty-two participants completed the quiet stance under varying visual (eyes open—EO; eyes closed—EC) and surface (Firm vs. Foam) conditions, with data collected from the smartphone, force plate, and motion capture systems. Intraclass correlation coefficients (ICCs) and Pearson correlation coefficients assessed the reliability and validity for all outcome measures (sway area and sway velocity). The results demonstrated reliability, with strong validity between the devices. A repeated-measures ANOVA found no significant differences between the devices. Postural outcomes revealed the significant main effects of both the visual (EO vs. EC) and surface (Firm vs. Foam) conditions. In conclusion, the study demonstrated the validity, sensitivity, and accuracy of the custom-designed smartphone app, offering the potential for bridging the gap between at-home and clinical balance assessments. Full article

This work presents a comparative study of low cost and low invasiveness sensors (plantar pressure and inertial measurement units) for classifying cross-country skiing techniques. A dataset was created for symmetrical comparative analysis, with data collected from skiers using instrumented insoles that measured plantar pressure, foot angles, and acceleration. A deep learning model based on CNN and LSTM was trained on various sensor combinations, ranging from two specific pressure sensors to a full multisensory array per foot incorporating 4 pressure sensors and an inertial measurement unit with accelerometer, magnetometer, and gyroscope. Results demonstrate an encouraging performance with plantar pressure sensors and classification accuracy closer to inertial sensing. The proposed approach achieves a global average accuracy of 94% to 99% with a minimal sensor setup, highlighting its potential for low-cost and precise technique classification in cross-country skiing and future applications in sports performance analysis. Full article

Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they are usually based on simulated falls. By using kinematic data from real near-fall incidents that occurred in physically demanding work environments, this study overcomes this limitation and improves the ecological validity of fall detection algorithms. This study systematically tests several machine-learning architectures for near-fall detection using the Prev-Fall dataset, which consists of high-resolution inertial measurement unit (IMU) data from 110 workers. Convolutional neural networks (CNNs), residual networks (ResNets), convolutional long short-term memory networks (convLSTMs), and InceptionTime models were trained and evaluated over a range of temporal window lengths using a neural architecture search. High-validation F1 scores were achieved by the best-performing models, particularly CNNs and InceptionTime, indicating their effectiveness in near-fall classification. The need for more contextual variables to increase robustness was highlighted by recurrent false positives found in subsequent tests on previously unobserved occupational data, especially during biomechanically demanding activities such as bending and squatting. Nevertheless, our findings suggest the applicability of machine-learning-based STF prevention systems for workplace safety monitoring and, more generally, applications in fall mitigation. To further improve the accuracy and generalizability of the system, future research should investigate multimodal data integration and improved classification techniques. Full article

The propeller state of unmanned aerial vehicles (UAV) is difficult to detect in real time due to trouble with laying out the sensor and multiple signal sources. To solve this problem, a fault detection method for multi-rotor UAV propellers was proposed based on a signal analysis of the built-in inertial measurement unit (IMU). Firstly, the multi-source coupled signals of the UAV flight were obtained through the ground station. Then, the picked-up signals were optimally separated according to the multi-rotor UAV propeller fault dynamics model, and signals rich in fault information were obtained. Finally, the separated signals were calculated using the symmetrized dot pattern (SDP), and then the similarity index was used to quantify the distribution of the signal in the feature plot to realize propeller fault detection. The OTSU algorithm was used to quantify the detection results, yielding a similarity of 76.2% in the z-axis direction, which is better than the values in the other two directions. The simulation and experimental analysis of the propeller failure dynamics model showed that the proposed method can effectively identify the propeller faults of multi-rotor UAVs. Full article

This paper presents a heading alignment procedure for drone navigation employing a single hover GNSS antenna combined with low-grade MEMs-IMU sensors. The design was motivated by the need for a drone-mounted differential interferometric SAR (DinSAR) application. Still, the methodology proposed here applies to any Unmanned Aerial Vehicle (UAV) application that requires high-precision navigation data for short-flight missions utilizing cost-effective MEMs sensors. The method proposed here involves a Bayesian parameter estimation based on a simultaneous cumulative Mahalanobis metric applied to the innovation process of Kalman-like filters, which are identical except for the initial heading guess. The procedure is then generalized to multidimensional parameters, thus called parametric alignment, referring to the fact that the strategy applies to alignment problems regarding some parameters, such as the heading initial value. The motivation for the multidimensional extension in the scenario is also presented. The method is highly applicable for cases where gyro-compassing is not available. It employs the most straightforward optimization techniques that can be implemented using a real-time parallelism scheme. Experimental results obtained from a real UAV mission demonstrate that the proposed method can provide initial heading alignment when the heading is not directly observable during takeoff, while numerical simulations are used to illustrate the extension to the multidimensional case. Full article

When the orchard operation platform is in use within the orchard, issues of tilting and overturning can arise due to uneven ground, necessitating instant leveling. In this study, the orchard operation platform is simplified into a four-point leveling mechanism, and an adaptive leveling system based on an inertial measurement unit (IMU) is designed. The relationship between coordinate transformation is utilized to derive the platform tilt angle and the position error relationship of the electric actuator, allowing for the analysis of the angle adjustment factors of the leveling mechanism. Through co-simulation using MATLAB and ADAMS, fuzzy control is implemented in addition to PID control, resulting in improved performance. A prototype model of the orchard operation platform is produced and tested, with the platform’s attitude angle remaining stable within a range of ±1.5°. The average leveling time is found to be within 3.6 s. The mean values of dynamic leveling inclination under PID and fuzzy PID control are 2.6° and 1.6°, respectively, with corresponding standard deviations of 1.4° and 0.8°. It conforms to the development trend of agricultural machinery electrification and intelligence and provides a reference basis for manufacturers. Full article

Inertial navigation systems (INSs) provide autonomous position estimation capabilities independent of global navigation satellite systems (GNSSs). However, the high cost of traditional sensors, such as fiber-optic gyroscopes (FOGs), limits their widespread adoption. In contrast, micro-electromechanical system (MEMS)-based inertial measurement units (IMUs) offer a low-cost alternative; however, their lower accuracy and sensor bias issues, particularly in maritime environments, remain considerable obstacles. This study proposes an improved method for bias estimation by comparing the estimated values from a trajectory generator (TG)-based acceleration and angular-velocity estimation system with actual measurements. Additionally, for X- and Y-axis accelerations, we introduce a method that leverages the correlation between altitude differences derived from an INS/GNSS/gyrocompass (IGG) and those obtained during the TG estimation process to estimate the bias. Simulation datasets from experimental voyages validate the proposed method by evaluating the mean, median, normalized cross-correlation, least squares, and fast Fourier transform (FFT). The Butterworth filter achieved the smallest angular-velocity bias estimation error. For X- and Y-axis acceleration bias, altitude-based estimation achieved differences of 1.2 × 10⁻² m/s² and 1.0 × 10⁻⁴ m/s², respectively, by comparing the input bias using 30 min data. These methods enhance the positioning and attitude estimation accuracy of low-cost IMUs, providing a cost-effective maritime navigation solution. Full article

The MEMS inertial sensors based on the pedestrian localization system assisted by the zero-velocity update (ZUPT) algorithm has gained widespread attention, due to its effective independent localization in indoor environments. However, in the realistic pedestrian localization test, the system often appears to drift because of the long-term error accumulation of inertial sensors and the limitation of the error suppression of traditional pedestrian localization algorithms. In this article, based on full analysis of existing constraint-based methods, a multi-condition constrained pedestrian localization algorithm is proposed, which integrates zero velocity detection based on phase threshold constraint, single and dual feet fusion constraint algorithms, to suppress drift and improve localization accuracy. The experimental results demonstrate that the multi-condition constraint algorithm can reduce localization errors by 59% compared to the unconstrained approach, and by 42% and 26% compared to algorithms using only single-foot or dual-feet constraints, respectively. The trajectory generated from the experiments further shows that the proposed algorithm produces a trajectory that more closely aligns with the actual walking path. Full article

Inertial Measurement Units (IMUs) are widely utilized in shoulder rehabilitation due to their portability and cost-effectiveness, but their reliance on spatial motion data restricts their use in comprehensive musculoskeletal analyses. To overcome this limitation, we propose SWCTNet (Sliding Window CNN + Channel-Time Attention Transformer Network), an advanced neural network specifically tailored for multichannel temporal tasks. SWCTNet integrates IMU and surface electromyography (sEMG) data through sliding window convolution and channel-time attention mechanisms, enabling the efficient extraction of temporal features. This model enables the prediction of muscle activation patterns and kinematics using exclusively IMU data. The experimental results demonstrate that the SWCTNet model achieves recognition accuracies ranging from 87.93% to 91.03% on public temporal datasets and an impressive 98% on self-collected datasets. Additionally, SWCTNet exhibits remarkable precision and stability in generative tasks: the normalized DTW distance was 0.12 for the normal group and 0.25 for the patient group when using the self-collected dataset. This study positions SWCTNet as an advanced tool for extracting musculoskeletal features from IMU data, paving the way for innovative applications in real-time monitoring and personalized rehabilitation at home. This approach demonstrates significant potential for long-term musculoskeletal function monitoring in non-clinical or home settings, advancing the capabilities of IMU-based wearable devices. Full article

While inertial measurement unit (IMU)-based systems have shown their potential in quantifying medically significant gait parameters, it remains to be determined whether they can provide accurate and reliable parameters both across various walking conditions and in healthcare settings. Using an IMU-based system we previously developed, with one IMU module on each subject’s heel, we quantify the gait parameters of 55 men and 46 women, all healthy and aged 40–65, in normal, dual-task, and fast walking conditions. We evaluate their intra-session reliability, and we establish a new reference database of such parameters showing good to excellent reliability. ICC(2,1) assesses relative reliability, while SEM% and MDC% assess absolute reliability. The reliability is excellent for all spatiotemporal gait parameters and the stride length (SL) symmetry ratio (ICC ≥ 0.90, SEM% ≤ 4.5%, MDC% ≤ 12.4%) across all conditions. It is good to excellent for the fast walking performance (FWP) indices of stride (Sr), stance (Sa), double-support (DS), and step (St) times; gait speed (GS); and the GS normalized to leg length (GS_n1) and body height (GS_n2) (ICC ≥ 0.91, |SEM%| ≤ 10.0%, |MDC%| ≤ 27.6%). Men have a higher swing time (Sw) and SL across all conditions. The following parameters are gender-independent: (1) Sa, DS, GS_n1, GS_n2; (2) the symmetry ratios of SL and GS, as well as Sa% and Sw% (representing Sa and Sw as percentages of Sr); and (3) the FWPs of Sr, Sa, Sw, DS, St, cadence, Sa% and Sw%. Our results provide reference values with new insights into gender FWP comparisons rarely reported in the literature. The advantages and reliability of our IMU-based system make it suitable in medical applications such as prosthetic evaluation, fall risk assessment, and rehabilitation. Full article