1,721,333 research outputs found
Characterization of the performance of memetic algorithms for the automation of bone tracking with fluoroscopy
No full textReliable knowledge of in vivo joint kinematics is fundamental in clinical medicine. Fluoroscopic motion tracking theoretically permits a millimeter/degree level of accuracy in 3-D joint motion analysis, but the reliability of the local optimization algorithm [Levenberg-Marquardt (LMA)], typically used for the pose estimation, is highly operator dependent. A new memetic algorithm (MA), hybridizing global evolution and a local search metaphor for learning, is proposed to automate the analysis and improve its reliability and robustness. The performance of MA was assessed for in silico and in vivo elbow kinematics, with and without user supervision. The best learning strategy between Lamarckian and Baldwinian evolution was identified. MA's accuracy and repeatability was quantified and compared with LMA's. The algorithm performed best using a partial Lamarckian learning strategy. The geometric symmetry of analyzed bony segments influenced the accuracy, whereas the absolute bone pose with respect to the projection geometry affected the repeatability. In contrast to LMA, MA provided robust, repeatable, and operator independent pose estimations, even for in vivo analyses. The pose can be automatically estimated with errors lower than 1 mm and 1° for all the pose parameters except the depth position, if the investigated motion task avoids symmetric bony projection silhouettes
Comparative analysis of 12 methods using wearable inertial sensors for gait parameters estimation during walking
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Analysis of the performance of 17 algorithms from a systematic review: Influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements
No full textBackground: The quantification of gait temporal parameters (i.e. step time, stance time) is crucial in human motion analysis and requires the accurate identification of gait events (i.e. heel strike, toe off). With the widespread use of inertial wearable sensors, many algorithms were proposed and applied for the purpose. Nevertheless, only few studies addressed the assessment of the actual performance of these algorithms, rather considering each proposed algorithm as a whole. Research question: How different implementation characteristics influence the assessment of gait events and temporal parameters from inertial sensor measures in terms of accuracy and repeatability? Methods: Seventeen different algorithms were identified from a systematic review and classified based on: 1) sensor position, 2) target variable, 3) computational approach. The influence of these characteristics was analysed on walking data of 35 healthy volunteers mounting 5 tri-axial inertial sensors. Foot contact events identified by 2 force platforms were assumed as gold standard. Temporal parameters were calculated from gait events. Algorithm performance was analysed in terms of accuracy (error median value) and repeatability (error 25th and 75th percentile values). Results: Shank- and foot-based algorithms performed better (in terms of accuracy and repeatability) in gait events detection and stance time estimation than lower trunk-based ones, while sensor position did not affect step estimate, given the error bias characteristics. Angular velocity-based algorithms performed significantly better than acceleration-based ones for toe off detection in terms of repeatability (68 ms and 102 ms, 25th–75th percentile error range, respectively) and, for heel strike detection, showed better repeatability (40 ms and 111 ms) and comparable accuracy (65 ms and 60 ms median error, respectively) than acceleration-based ones. The performance of different computational approaches varied depending on sensor positioning. Significance: Present results support the selection of the proper algorithm for the estimation of gait events and temporal parameters in relation to the specific application
Wearable inertial sensors in swimming motion analysis: a systematic review
No full textThe use of contemporary technology is widely recognised as a key tool for enhancing competitive performance in swimming. Video analysis is traditionally used by coaches to acquire reliable biomechanical data about swimming performance; however, this approach requires a huge computational effort, thus introducing a delay in providing quantitative information. Inertial and magnetic sensors, including accelerometers, gyroscopes and magnetometers, have been recently introduced to assess the biomechanics of swimming performance. Research in this field has attracted a great deal of interest in the last decade due to the gradual improvement of the performance of sensors and the decreasing cost of miniaturised wearable devices. With the aim of describing the state of the art of current developments in this area, a systematic review of the existing methods was performed using the following databases: PubMed, ISI Web of Knowledge, IEEE Xplore, Google Scholar, Scopus and Science Direct. Twenty-seven articles published in indexed journals and conference proceedings, focusing on the biomechanical analysis of swimming by means of inertial sensors were reviewed. The articles were categorised according to sensor's specification, anatomical sites where the sensors were attached, experimental design and applications for the analysis of swimming performance. Results indicate that inertial sensors are reliable tools for swimming biomechanical analyses
Gait Kinematic Analysis in Water Using Wearable Inertial Magnetic Sensors.
No full textWalking is one of the fundamental motor tasks executed during aquatic therapy. Previous kinematics analyses conducted using waterproofed video cameras were limited to the sagittal plane and to only one or two consecutive steps. Furthermore, the set-up and post-processing are time-consuming and thus do not allow a prompt assessment of the correct execution of the movements during the aquatic session therapy. The aim of the present study was to estimate the 3D joint kinematics of the lower limbs and thorax-pelvis joints in sagittal and frontal planes during underwater walking using wearable inertial and magnetic sensors. Eleven healthy adults were measured during walking both in shallow water and in dry-land conditions. Eight wearable inertial and magnetic sensors were inserted in waterproofed boxes and fixed to the body segments by means of elastic modular bands. A validated protocol (Outwalk) was used. Gait cycles were automatically segmented and selected if relevant intraclass correlation coefficients values were higher than 0.75. A total of 704 gait cycles for the lower limb joints were normalized in time and averaged to obtain the mean cycle of each joint, among participants. The mean speed in water was 40% lower than that of the dry-land condition. Longer stride duration and shorter stride distance were found in the underwater walking. In the sagittal plane, the knee was more flexed (≈ 23°) and the ankle more dorsiflexed (≈ 9°) at heel strike, and the hip was more flexed at toe-off (≈ 13°) in water than on land. On the frontal plane in the underwater walking, smoother joint angle patterns were observed for thorax-pelvis and hip, and ankle was more inversed at toe-off (≈ 7°) and showed a more inversed mean value (≈ 7°). The results were mainly explained by the effect of the speed in the water as supported by the linear mixed models analysis performed. Thus, it seemed that the combination of speed and environment triggered modifications in the joint angles in underwater gait more than these two factors considered separately. The inertial and magnetic sensors, by means of fast set-up and data analysis, can supply an immediate gait analysis report to the therapist during the aquatic therapy session
Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review
Full text linkRecent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends
Evaluation of WIMU Sensor Performance in Estimating Running Stride and Vertical Stiffness in Football Training Sessions: A Comparison with Smart Insoles
Full text linkTemporal parameters are crucial for understanding running performance, especially in elite sports environments. Traditional measurement methods are often labor-intensive and not suitable for field conditions. This study seeks to provide greater clarity in parameter estimation using a single device by comparing it to the gold standard. Specifically, this study aims to investigate how the temporal parameters and vertical stiffness (Kvert) of running stride exerted by IMU sensors are related to the parameters of the smart insole for outdoor acquisition. Ten healthy male subjects performed four 60-meter high-speed runs. Data were collected using the WIMU PRO™ device and smart insoles. Contact time (CT) and flight time (FT) were identified, and Kvert was calculated using Morin’s method. Statistical analyses assessed data normality, correlations, and reliability. WIMU measured longer CT, with differences ranging from 26.3% to 38.5%, and shorter FT, with differences ranging from 27.3% to 54.5%, compared to smart insoles, across different running speeds. Kvert values were lower with WIMU, with differences ranging from 23.96% to 45.01% depending on the running activity, indicating significant differences (p < 0.001). Using these results, a multiple linear regression model was developed for the correction of WIMU’s Kvert values, improving the accuracy. The improved accuracy of Kvert measurements has significant implications for athletic performance. It provides sports scientists with a more reliable metric to estimate player fatigue, potentially leading to more effective training regimens and injury prevention strategies. This advancement is particularly valuable in team sports settings, where easy-to-use and accurate biomechanical assessments of multiple athletes are essential
Exploring the Relationship Between the Acute:Chronic Workload Ratio and Running Parameters in Elite Football Athletes
Full text linkIn contemporary sports science, the integration of wearable inertial measurement units (IMUs) has revolutionized athlete performance monitoring, offering insights into training load management and injury risk mitigation. The acute:chronic workload ratio (ACWR) has emerged as a pivotal metric, indicating the balance between acute training stress and chronic adaptation. This study investigates the relationship between ACWR and running parameters, i.e., contact time (CT), flight time (FT), and vertical stiffness (Kvert). Data from thirty-five elite male soccer players were analyzed using the WIMU Pro system. Statistical analyses showed that CT increased with workload, with significant differences observed between athletes in the sweet spot and others in the danger zone (p vert values were consistently lower in athletes in the danger zone across all workload indicators (p < 0.001), with large effect sizes going up to 0.94. Conversely, FT showed no significant variation between ACWR groups. These findings suggest that elevated ACWRs may be linked to reductions in vertical stiffness, highlighting a potential increase in risk of injury. Coaches and practitioners can utilize these insights to tailor training programs, integrating load monitoring with tactical considerations to optimize athlete performance. Understanding the nuanced interplay between workload ratios and biomechanical parameters provides valuable insights for performance optimization for elite football athletes
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