Search Results (1,438)

Investigating underwater environments using Multi-Beam Echo Sounder (MBES) point cloud registration technology is a critical yet underdeveloped area in oceanographic research. This paper presents a fast, deterministic Branch-and-Bound (BnB) method with four degrees of freedom, which combines Inertial Measurement Unit (IMU) data with MBES point cloud data for precise registration. Given the prevalence of outliers and noise in underwater acoustic measurements, the BnB method is employed to provide globally deterministic solutions. However, due to the exponential convergence speed of the BnB method with respect to the dimensionality of the solution space, searching within a six-degree-of-freedom parameter space (three rotational and three translational degrees of freedom) can be extremely time-consuming. To this end, the Z-axis of the point cloud is aligned with the gravitational direction of the IMU, reducing the rotational degrees of freedom from three to one, specifically concerning yaw. Additionally, an outlier exclusion strategy is introduced to eliminate mismatches, significantly reducing the number of key-point correspondences and thereby improving registration efficiency. Experiments conducted on both public and real-world lake datasets demonstrate that the proposed method achieves a favorable balance between speed and accuracy, outperforming other tested methods and meeting the demands of contemporary research. Full article

Determining whether preoperative performance-based knee function predicts postoperative performance-based knee function and whether patient-reported outcome measures (PROMs) completed by participants can detect these changes could significantly enhance the planning of postoperative rehabilitation for patients following total knee arthroplasty (TKA). This study aims to collect data on performance-based knee function using inertial measurement units (IMUs) worn by participants both preoperatively and postoperatively. PROMs will be completed by the patients before and after surgery to assess their ability to detect the same changes in performance-based knee function measured by the sensors. Additionally, the study will investigate the correlation between the degree of knee alignment correction and postoperative performance-based knee function in participants after TKA. Full article

The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for lifting provides risk zones for assessing two-handed lifting tasks. This paper describes two computational models for identifying the lifting risk zones using gyroscope information from five inertial measurement units (IMUs) attached to the lifter. Two models were developed: (1) the ratio model using body segment length ratios of the forearm, upper arm, trunk, thigh, and calf segments, and (2) the ratio + length model using actual measurements of the body segments in the ratio model. The models were evaluated using data from 360 lifting trials performed by 10 subjects (5 males and 5 females) with an average age of 51.50 (±9.83) years. The accuracy of the two models was compared against data collected by a laboratory-based motion capture system as a function of 12 ACGIH lifting risk zones and 3 grouped risk zones (low, medium, and high). Results showed that only the ratio + length model provides acceptable estimates of lifting risk with an average of 69% accuracy level for predicting one of the 3 grouped zones and a higher rate of 92% for predicting the high lifting zone. Full article

Unmanned aerial vehicle (UAV) systems have recently become essential for mapping, surveying, and three-dimensional (3D) modeling applications. These systems are capable of providing highly accurate products through integrated advanced technologies, including a digital camera, inertial measurement unit (IMU), and Global Navigation Satellite System (GNSS). UAVs are a cost-effective alternative to traditional aerial photogrammetry, and recent advancements demonstrate their effectiveness in many applications. In UAV-based photogrammetry, ground control points (GCPs) are utilized for georeferencing to enhance positioning precision. The distribution, number, and location of GCPs in the study area play a crucial role in determining the accuracy of photogrammetric products. This research evaluates the accuracy of positioning techniques for image acquisition for photogrammetric production and the effect of GCP distribution models. The camera position was determined using real-time kinematic (RTK), post-processed kinematic (PPK), and precise point positioning-ambiguity resolution (PPP-AR) techniques. In the criteria for determining the GCPs, six models were established within the İstanbul Technical University, Ayazaga Campus. To assess the accuracy of the points in these models, the horizontal, vertical, and 3D root mean square error (RMSE) values were calculated, holding the test points stationary in place. In the study, 2.5 cm horizontal RMSE and 3.0 cm vertical RMSE were obtained with the model containing five homogeneous GCPs by the indirect georeferencing method. The highest RMSE values of all three components in RTK, PPK, and PPP-AR methods were obtained without GCPs. For all six models, all techniques have an error value of sub-decimeter. The PPP-AR technique yields error values that are comparable to those of the other techniques. The PPP-AR appears to be an alternative to RTK and PPK, which usually require infrastructure, labor, and higher costs. Full article

Background/Objectives: Gait analysis, traditionally performed with lab-based optical motion capture systems, offers high accuracy but is costly and impractical for real-world use. Wearable technologies, especially inertial measurement units (IMUs), enable portable and accessible assessments outside the lab, though challenges with sensor placement, signal selection, and algorithm design can affect accuracy. This systematic review aims to bridge the benchmarking gap between IMU-based and traditional systems, validating the use of wearable inertial systems for gait analysis. Methods: This review examined English studies between 2012 and 2023, retrieved from the Scopus database, comparing wearable sensors to optical motion capture systems, focusing on IMU body placement, gait parameters, and validation metrics. Exclusion criteria for the search included conference papers, reviews, unavailable papers, studies without wearable inertial sensors for gait analysis, and those not involving agreement studies or optical motion capture systems. Results: From an initial pool of 479 articles, 32 were selected for full-text screening. Among them, the lower body resulted in the most common site for single IMU placement (in 22 studies), while the most frequently used multi-sensor configuration involved IMU positioning on the lower back, shanks, feet, and thighs (10 studies). Regarding gait parameters, 11 studies out of the 32 included studies focused on spatial-temporal parameters, 12 on joint kinematics, 2 on gait events, and the remainder on a combination of parameters. In terms of validation metrics, 24 studies employed correlation coefficients as the primary measure, while 7 studies used a combination of error metrics, correlation coefficients, and Bland–Altman analysis. Validation metrics revealed that IMUs exhibited good to moderate agreement with optical motion capture systems for kinematic measures. In contrast, spatiotemporal parameters demonstrated greater variability, with agreement ranging from moderate to poor. Conclusions: This review highlighted the transformative potential of wearable IMUs in advancing gait analysis beyond the constraints of traditional laboratory-based systems. Full article

The analysis of running gait has conventionally taken place within an expensive and restricted laboratory space, with wearable technology offering a practical, cost-effective, and unobtrusive way to examine running gait in more natural environments. This pilot study presents a wearable inertial measurement unit (IMU) setup for the continuous analysis of running gait during an outdoor parkrun (i.e., 5 km). The study aimed to (1) provide analytical validation of running gait measures compared to time- and age-graded performance and (2) explore performance validation. Ten healthy adults (7 females, 3 males, mean age 37.2 ± 11.7 years) participated. The participants wore Axivity AX6 IMUs on the talus joint of each foot, recording tri-axial accelerometer and gyroscope data at 200 Hz. Temporal gait characteristics—gait cycle, ground contact time, swing time, and duty factor—were extracted using zero-crossing algorithms. The data were analyzed for correlations between the running performance, foot strike type, and fatigue-induced changes in temporal gait characteristics. Strong correlations were found between the performance time and both the gait cycle and ground contact time, with weak correlations for foot strike types. The analysis of asymmetry and fatigue highlighted modest changes in gait as fatigue increased, but no significant gender differences were found. This setup demonstrates potential for in-field gait analysis for running, providing insights for performance and injury prevention strategies. Full article

To address the shortcomings of light detection and ranging (LiDAR) sensors in extracting road surface elevation information in front of a vehicle, a scheme for digital terrain construction based on the fusion of an Inertial Measurement Unit (IMU) and LiDAR perception is proposed. First, two sets of sensor coordinate systems were configured, and the parameters of LiDAR and IMU were calibrated. Then, a terrain construction system based on the fusion perception of IMU and LiDAR was established, and improvements were made to the state estimation and mapping architecture. Terrain construction experiments were conducted in an academic setting. Finally, based on the output information from the terrain construction system, a moving average-like algorithm was designed to process point cloud data and extract the road surface elevation information at the vehicle’s trajectory position. By comparing the extraction effects of four different sliding window widths, the 4 cm width sliding window, which yielded the best results, was ultimately selected, making the extracted road surface elevation information more accurate and effective. Full article

Human hands have over 20 degrees of freedom, enabled by a complex system of bones, muscles, and joints. Hand differences can significantly impair dexterity and independence in daily activities. Accurate assessment of hand function, particularly digit movement, is vital for effective intervention and rehabilitation. However, current clinical methods rely on subjective observations and limited tests. Smart gloves with inertial measurement unit (IMU) sensors have emerged as tools for capturing digit movements, yet their sensor accuracy remains underexplored. This study developed and validated an IMU-based smart glove system for measuring finger joint movements in individuals with hand differences. The glove measured 3D digit rotations and was evaluated against an industrial robotic arm. Tests included rotations around three axes at 1°, 10°, and 90°, simulating extension/flexion, supination/pronation, and abduction/adduction. The IMU sensors demonstrated high accuracy and reliability, with minimal systematic bias and strong positive correlations (p > 0.95 across all tests). Agreement matrices revealed high agreement (<1°) in 24 trials, moderate (1–10°) in 12 trials, and low (>10°) in only 4 trials. The Root Mean Square Error (RMSE) ranged from 1.357 to 5.262 for the 90° tests, 0.094 to 0.538 for the 10° tests, and 0.129 to 0.36 for the 1° tests. Likewise, mean absolute error (MAE) ranged from 0.967 to 4.679 for the 90° tests, 0.073 to 0.386 for the 10° tests, and 0.102 to 0.309 for the 1° tests. The sensor provided precise measurements of digit angles across 0–90° in multiple directions, enabling reliable clinical assessment, remote monitoring, and improved diagnosis, treatment, and rehabilitation for individuals with hand differences. Full article

Handheld LiDAR scanners, which typically consist of a LiDAR sensor, Inertial Measurement Unit, and processor, enable data capture while moving, offering flexibility for various applications, including indoor and outdoor 3D mapping in fields such as architecture and civil engineering. Unlike fixed LiDAR systems, handheld devices allow data collection from different angles, but this mobility introduces challenges in data quality, particularly when initial calibration between sensors is not precise. Accurate LiDAR-IMU calibration, essential for mapping accuracy in Simultaneous Localization and Mapping applications, involves precise alignment of the sensors’ extrinsic parameters. This research presents a robust initial pose calibration method for LiDAR-IMU systems in handheld devices, specifically designed for indoor environments. The research contributions are twofold. Firstly, we present a robust plane detection method for LiDAR data. This plane detection method removes the noise caused by mobility of scanning device and provides accurate planes for precise LiDAR initial pose estimation. Secondly, we present a robust planes-aided LiDAR calibration method that estimates the initial pose. By employing this LiDAR calibration method, an efficient LiDAR-IMU calibration is achieved for accurate mapping. Experimental results demonstrate that the proposed method achieves lower calibration errors and improved computational efficiency compared to existing methods. Full article

The accurate measurement of spatiotemporal parameters, such as step length and step frequency, is crucial for analyzing running and sprinting performance. Traditional methods like video analysis and force platforms are either time consuming or limited in scope, prompting the need for more efficient technologies. This study evaluates the effectiveness of a commercial Global Positioning System (GPS) unit integrated with an Inertial Measurement Unit (IMU) in capturing these parameters during sprints at varying velocities. Five experienced male runners performed six 40 m sprints at three velocity conditions (S: Slow, M: Medium, F: Fast) while equipped with a GPS-IMU system and an optical system as the gold standard reference. A total of 398 steps were analyzed for this study. Step frequency, step length and step velocity were extracted and compared using statistical methods, including the coefficient of determination (r²) and root mean square error (RMSE). Results indicated a very large agreement between the embedded system and the reference system, for the step frequency (r² = 0.92, RMSE = 0.14 Hz), for the step length (r² = 0.91, RMSE = 0.07 m) and the step velocity (r² = 0.99, RMSE = 0.17 m/s). The GPS-IMU system accurately measured spatiotemporal parameters across different running velocities, demonstrating low relative errors and high precision. This study demonstrates that GPS-IMU systems can provide comprehensive spatiotemporal data, making them valuable for both training and competition. The integration of these technologies offers practical benefits, helping coaches better understand and enhance running performance. Future improvements in sample rate acquisition GPS-IMU technology could further increase measurement accuracy and expand its application in elite sports. Full article

It is important to improve the localization accuracy of the unmanned surface vehicle (USV) for ensuring safe navigation in an inland waterway scenario. However, the localization accuracy of the USV is affected by the limited availability of global navigation satellite system signals, the sparsity of feature points, and the high scene similarity in inland waterway scenarios. Therefore, this paper proposes a laser-based simultaneous localization and mapping (SLAM) algorithm for accurate localization and mapping in inland waterway scenarios. Inertial measurement unit (IMU) data are integrated with lidar data to address motion distortion caused by the frequent motion of the USV. Subsequently, a generalized iterative closest point (GICP) algorithm incorporating rejection sampling is integrated to enhance the accuracy of point cloud matching, involving a two-phase filtering process to select key feature points for matching. Additionally, a mixed global descriptor is constructed by combining point cloud intensity and distance information to improve the accuracy of loop closure detection. Experiments are conducted on the USV-Inland datasets to evaluate the performance of the proposed algorithm. The experimental results show that the proposed algorithm generates accurate mapping and significantly improves localization accuracy by 25.6%, 18.5%, and 23.6% compared to A-LOAM, LeGO-LOAM, and ISC-LOAM, respectively. These results demonstrate that the proposed algorithm achieves accurate localization and mapping in an inland waterway scenario. Full article

Background: We aimed to assess (1) the awareness of parents regarding the cervical rotation preference of their infant and the agreement of the parent, clinician and objective assessments, and (2) the test–retest reliability for objective (measured) rotation, lateral flexion and combined flexion–rotation. Methods: This was a cross-sectional study including 69 infants aged three to six months with upper cervical spine dysfunction, without general health issues or specific cervical spine impairments. No treatment was applied. The primary outcomes were parent and clinician assessments of cervical spine rotation preference. The secondary outcome was the cervical range of motion measured by inertial measurement units (IMUs) at two different timepoints. Spearman correlation was performed for the parent, clinician and objective assessments. IMU data were dichotomized into the preferred and unpreferred sides, and test–retest reliability was assessed (ICC). Results: The mean age of infants was 145 days ± 29.1 days, birth length 49.40 cm ± 2.7 cm, birth weight 3328 g ± 530.9 g and 24 were female. In total, 33 infants were assessed by their parents as right-preferred, 30 as left-preferred and 6 as having no preference. The clinician assessed 38 infants as right-preferred and 31 as left-preferred. The correlation between parents and the clinician was r_s = 0.687 (p < 0.001), the clinician and the IMU r_s = 0.408 (p = 0.005) and parents and the IMU r_s = 0.301 (p = 0.044). The ICC of cervical range of motion measurements ranged from poor to moderate. Conclusions: Clinicians can use the parents’ assessment of cervical spine rotation preference as a foundation for their clinical examination. IMU measurements are difficult in infants, possibly due to their lack of cooperation during measurements. Clinical Trial Registration Number: clinicaltrails.gov (NCT04981782). Full article

Swimmer’s shoulder is a common condition among elite swimmers, often leading to pain and reduced performance. Fatigue can exacerbate this condition by affecting shoulder strength, proprioception, and range of motion, potentially increasing the risk of overuse injuries. This preliminary study aimed to evaluate the impact of physiotherapy treatment and the effects of fatigue on shoulder kinematics using inertial and magnetic measurement units (IMUs). Five male swimmers (aged 21–27) with at least 3 years of training and suffering from swimmer’s shoulder pain participated in the study. The protocol included three sessions: dry front crawl exercises using one arm in the first and third sessions, and a fatiguing swimming exercise in the second. IMUs were used to capture 3D rotation angles, focusing on flexion/extension, abduction/adduction, and internal/external rotations during the first and third sessions. Stroke amplitude was analyzed before and after the physiotherapy treatment and fatiguing exercise. The results showed a significant increase in internal/external rotation amplitude post-fatigue before physiotherapy (p = 0.03), with a non-significant decrease in flexion/extension after treatment, suggesting improved shoulder stabilization. Despite these preliminary findings being based on a reduced number of participants, they indicate that physiotherapy may enhance shoulder motion control in swimmers with shoulder pain. Nevertheless, further studies with larger cohorts are needed to confirm these results. Full article

The continuous, automated monitoring of sensor-based data for walking capacity and mobility has expanded gait analysis applications beyond controlled laboratory settings to real-world, everyday environments facilitated by the development of portable, cost-efficient wearable sensors. In particular, the integration of Inertial Measurement Units (IMUs) into smart shoes has proven effective for capturing detailed foot movements and spatiotemporal gait characteristics. While IMUs enable accurate foot trajectory estimation through the double integration of acceleration data, challenges such as drift errors necessitate robust correction techniques to ensure reliable performance. This review analyzes current literature on shoe-based systems utilizing IMUs to estimate spatiotemporal gait parameters and foot trajectory characteristics, including foot–ground clearance. We explore the challenges and advancements in achieving accurate 3D foot trajectory estimation using IMUs in smart shoes and the application of advanced techniques like zero-velocity updates and error correction methods. These developments present significant opportunities for achieving reliable and efficient real-time gait assessment in everyday environments. Full article

Vertical jump height from a countermovement jump is a widespread metric to assess the lower limb functionality. Motion capture systems and force platforms are considered gold standards to estimate vertical jump height; however, their use in ecological settings is limited. This study aimed to evaluate the feasibility of low-sampling-rate inertial measurement units as an alternative to the gold standard systems. The validity of three computational methods for IMU-based data—numerical double integration, takeoff velocity, and flight time—was assessed using data from 18 healthy participants who performed five double-leg and ten single-leg countermovement jumps. The data were simultaneously collected from a motion capture system, two force platforms, and an IMU positioned at the L5 level. The comparisons revealed that the numerical double integration method exhibited the highest correlation (0.87) and the lowest bias (2.5 cm) compared to the gold standards and excellent reliability (0.88). Although the takeoff velocity and flight time methods demonstrated comparable performances for double-leg jumps, their accuracy in single-leg jumps was reduced. Overall, the low-sampling-rate IMU with the numerical double integration method seems to be a reliable and feasible alternative for field-based countermovement jump assessment, warranting future investigation across diverse populations and jump modalities. Full article