Movement Accuracy Research Articles

Diagnosing homo digitalis: towards a standardized assessment for digital tool competencies.

In the 21st century, digital devices have become integral to our daily lives. Still, practical assessments designed to evaluate an individual's digital tool competencies are absent. The present study introduces the "Digital Tools Test" ("DIGI"), specifically designed for the evaluation of one's proficiency in handling common applications and functions of smartphones and tablets. The DIGI assessment has been primarily tailored for prospective use among older adults and neurological patients with the latter frequently suffering from so-called apraxia, which potentially also affects the handling of digital tools. Similar to traditional tool use tests that assess tool-selection and tool-action processes, the DIGI assessment evaluates an individual's ability to select an appropriate application for a given task (e.g., creating a new contact), their capacity to navigate within the chosen application and their competence in executing precise and accurate movements, such as swiping. We tested the implementation of the DIGI in a group of 16 healthy adults aged 18 to 28 years and 16 healthy adults aged 60 to 74 years. All participants were able to withstand the assessment and reported good acceptance. The results revealed a significant performance disparity, with older adults displaying notably lower proficiency in the DIGI. The DIGI performance of older adults exhibited a correlation with their ability to employ a set of novel mechanical tools, but not with their ability to handle a set of familiar common tools. There was no such correlation for the younger group. In conclusion, this study introduces an innovative assessment tool aimed at evaluating common digital tool competencies. Our preliminary results demonstrate good acceptance and reveal expected group differences. For current cohorts of older adults, the results seem to indicate that the ability to use novel tools may aid digital tool use. In the next step, the psychometric properties of the DIGI assessment should be evaluated in larger and more diverse samples. The advancement of digital tool competency assessments and rehabilitation strategies is essential when we aim at facilitating societal inclusion and participation for individuals in affected populations.

MOVEMENT DATA ANALYSIS AS A NOVEL PREDICTIVE TECHNOLOGY FOR TENDON REPAIR

Digital Ventilated Cages (DVC) offer an innovative technology to obtain accurate movement data from a single mouse over time [1]. Thus, they could be used to determine the occurrence of a tendon damage event as well as inform on tissue regeneration [2,3]. Therefore, using the mouse model of tendon experimental damage, in this study it has been tested whether the recovery of tissue microarchitecture and of extracellular matrix (ECM) correlates with the motion data collected through this technology.Mice models were used to induce acute injury in Achilles tendons (ATs), while healthy ones were used as control. During the healing process, the mice were housed in DVC cages (Tecniplast) to monitor animal welfare and to study biomechanics assessing movement activity, an indicator of the recovery of tendon tissue functionality. After 28 days, the AT were harvested and assessed for their histological and immunohistochemical properties to obtain a total histological score (TSH) that was then correlated to the movement data.DVC cages showed the capacity to distinguish activity patterns in groups from the two different conditions. The data collected showed that the mice with access to the mouse wheel had a higher activity as compared to the blocked wheel group, which suggests that the extra movement during tendon healing improved motion ability. The histological results showed a clear difference between different analyzed groups. The bilateral free wheel group showed the best histological recovery, offering the highest TSH score, thus confirming the results of the DVC cages and the correlation between movement activity and structural recovery.Data obtained showed a correlation between TSH and the DVC cages, displaying structural and movement differences between the tested groups. This successful correlation allows the usage of DVC type cages as a non-invasive method to predict tissue regeneration and recovery.Acknowledgements: This research is part of the P4FIT project ESR13, funded by the H2020-ITN-EJD MSCA grant agreement No.955685.

Reflections on Rhythm Control in the Stage Performance Process of Dancers

Abstract The article proposes a dance score generation model based on the graph attention model and Transformer, which first preprocesses the stage motion capture data of dancers, utilizes a multi-scale attention network to realize motion feature aggregation, and introduces the gated loop unit in the Transformer model to enhance the recognition accuracy of dance movements. Based on the recognition of the dancer’s stage movements, the skeleton size normalization is performed on the joints of the dancer’s gestures, and the skeleton replacement method is combined to realize the correction of the dance movements. Next, we test the model’s dance score generation effect, the matching accuracy of dance movements and music rhythm, and evaluate and analyze the quality of dance movement correction. The results show that the Former model in this paper scores 8.33% higher in beat consistency than the traditional dance generation model Bailando, and the matching coefficient of the rhythmic intensity of the generated dance is basically the same as that of the original dance, with a difference of only 0.02-0.06. It shows that the rhythmic control of the stage performance of dancers carried out by the Transformer model can enhance the stage performance power of dancers and strengthen their stage performance. It shows that using the Transformer model to control the rhythm of dancers’ stage performances can improve the stage performance of dancers and enhance the viewability of stage performances.

Sensorimotor functioning changes in response to global exercise versus handwriting upper limb exercise training in Parkinson's disease, results from a phase II randomised controlled trial.

People with Parkinson's disease (PwPD) present motor alterations which can impact daily life tasks that require speed and/or accuracy of movement. A sub analysis of NCT01439022, aiming to estimate the extent to which two different exercise training protocols (global and handwriting upper limb exercise training) impact reaction time, travel speed, and accuracy in PwPD. Seventy PwPD, right-side dominant were randomised 1:1 into two six-month training protocol groups; 35 PwPD performed global exercise training and 35 performed specific training (handwriting upper limb exercise movements). Assessments of speed-accuracy and trade-off were carried out at baseline, after 3 and 6 months of training, and at a 12-month follow-up. The current study used data from a previous publication of a randomised controlled trial that included a 6-month self-managed community exercise programme for PwPD. For the present study we included only the participants who completed the Fitts' task during the baseline assessment. In the upper limb assessments, no main effects were found for the number of touches, but the exercise group showed a marginal increase over time on the left side. Error averages on the left side decreased significantly for the exercise group from baseline to 6 and 12 months. The exercise group also presented a lower Error CoV and the Reaction Time CoV increased on the right side. Significant findings for Fitts r on the left side indicated lower values for the exercise group, with improvements continuing at 12 months. We report the potential of global exercise interventions to facilitate improvements in reaction time and travel speed, as well as other motor control metrics, with lasting effects at 12 months, particularly on the non-dominant side.

Residual self-calibrated network with multi-scale channel attention for accurate EOG-based eye movement classification.

Recently, Electrooculography-based Human-Computer Interaction (EOG-HCI) technology has gained widespread attention in industrial areas, including assistive robots, augmented reality in gaming, etc. However, as the fundamental step of EOG-HCI, accurate eye movement classification (EMC) still faces a significant challenge, where their constraints in extracting discriminative features limit the performance of most existing works. To address this issue, a Residual Self-Calibrated Network with Multi-Scale Channel Attention (RSCA), focusing on efficient feature extraction and enhancement is proposed. The RSCA network first employs three self-calibrated convolution blocks within a hierarchical residual framework to fully extract the discriminative multi-scale features. Then, a multi-scale channel attention module adaptively weights the learned features to screen out the discriminative representation by aggregating the multi-scale context information along the channel dimension, thus further boosting the performance. Comprehensive experiments were performed using 5 public datasets and 7 prevailing methods for comparative validation. The results confirm that the RSCA network outperforms all other methods significantly, establishing a state-of-the-art benchmark for EOG-based EMC. Furthermore, thorough ablation analyses confirm the effectiveness of the employed modules within the RSCA network, providing valuable insights for the design of EOG-based deep models.

Accuracy of Invisalign® aligners in adult patients: a retrospective study of angular tooth movements.

This retrospective study aimed to assess the predictability of Invisalign® aligners regarding rotational, mesio-distal and buccal-lingual tip movements. Two materials were included in the analysis - EX30, used until 2013; and SmartTrack, in current use. The study comprised 56 adult patients treated with Invisalign Comprehensive. Data sample were assessed on three sets of digital models; model 1 - initial, model 2 - predicted, and model 3 - achieved. Sixty reference points were marked in each dental arch, and two reference planes assisted the superimposition. The degree of rotation, mesio-distal and buccal-lingual tip was obtained via trigonometric calculations, through a previously published validated method. The accuracy of outcomes was compared according to the types of tooth movement and teeth groups,and the influence of predetermined variables on movement accuracy was also investigated. Rotation and mesio-distal tip did not present any significant difference when comparing EX30 and SmartTrack groups. Only buccal-lingual tip presented a significant difference, incisor and canine groups treated with EX30 aligners presented an increase in accuracy (p= 0.007 and p = 0.007, respectively). For each additional degree planned for rotation movements, there was an increase of 0.35° in the discrepancy, and an increase of 0.40° and 0.41° for mesio-distal and buccal-lingual tip, respectively. EX30 and SmartTrack discrepancies were compared by multilevel linear regression. EX30 aligners reached higher accuracy for buccal-lingual tip in anterior teeth. However, for rotation and mesio-distal tip, SmartTrack and EX30 are similarly accurate. The total amount of planned movement has a significant impact on accuracy rates, with a decrease in accuracy for every additional degree.

Investigation of An Inverted Bow on Frigate Hull Resistance

This study discusses the inverted bow design on the combatant hull form. Changes in the shape of the stem angle and flare bow are used as analytical parameters to investigate the ship's performance. Ship resistance and motion will be predicted using the Computational Fluid Dynamics (CFD) approach using the Reynolds Averaged Navier Stokes (RANS) equation and the k-ε turbulence model. The volume of fluid (VOF) method is applied to simulate the change in the free surface between water and air using an overset mesh technique. The ship's movement is limited to sinkage and trim motions, so the movement's accuracy can be predicted. The results revealed that the inverted bow reduced the total resistance by 6.30%, whereas the trim and sinkage showed no significant changes. The breakdown of the reduction ratio showed that friction resistance components were reduced by 10.62%, wave resistance by 44.05%, and viscous-pressure resistance by 45.33%. This highlights the effectiveness of an inverted bow in optimizing wave and viscous pressure, enhancing overall ship performance.

Learn2Talk: 3D Talking Face Learns from 2D Talking Face.

The speech-driven facial animation technology is generally categorized into two main types: 3D and 2D talking face. Both of these have garnered considerable research attention in recent years. However, to our knowledge, the research into 3D talking face has not progressed as deeply as that of 2D talking face, particularly in terms of lip-sync and perceptual mouth movements. The lip-sync necessitates an impeccable synchronization between mouth motion and speech audio. The speech perception derived from the perceptual mouth movements should resemble that of the driving audio. To mind the gap between the two sub-fields, we propose Learn2Talk, a learning framework that enhances 3D talking face network by integrating two key insights from the field of 2D talking face. Firstly, drawing inspiration from the audio-video sync network, we develop a 3D sync-lip expert model for the pursuit of lip-sync between audio and 3D facial motions. Secondly, we utilize a teacher model, carefully chosen from among 2D talking face methods, to guide the training of the audio-to-3D motions regression network, thereby increasing the accuracy of 3D vertex movements. Extensive experiments demonstrate the superiority of our proposed framework over state-of-the-art methods in terms of lip-sync, vertex accuracy and perceptual movements. Finally, we showcase two applications of our framework: audio-visual speech recognition and speech-driven 3D Gaussian Splatting-based avatar animation. The project page of this paper is: https://lkjkjoiuiu.github.io/Learn2Talk/.

A Study on Semantic Classification of Guangxi Ethnic Folk Dance Movements Incorporating Deep Learning

Abstract In recent years, the development of Guangxi’s national folk dance has been on the rise and has gained much attention. The research of Guangxi’s national folk dance is currently in a booming period. The research is based on deep learning theory, using stack denoising autoencoder and convolutional depth Boltzmann mechanism to build a SdAE-CDBM model for dance movement classification. The dance movements are recognized and detected by using feature mining and extraction of dance movements in Guangxi folk dance videos. The SdAE-CDBM model of this paper is compared with other classification models in terms of semantic classification accuracy of dance movements to explore the classification performance of the SdAE-CDBM model proposed in this paper. The average F1 values of the SdAE-CDBM model in the classification of the seven types of dance movements are 86.77%, 88.54%, and 90.18%, respectively, which are the maximum values among the movement classification models. The SdAE-CDBM model was able to achieve the highest classification accuracy and the fastest classification convergence speed among all classification models. When it comes to classifying dance movements semantically, the SdAE-CDBM model achieves a classification accuracy of 70.28%, which is significantly superior to other classification models. The SdAE-CDBM model in this paper is highly effective in the semantic classification of dance movements, as evidenced by this.

Geometry and Kinematics of the Mecanum Wheel on a Plane and a Sphere

This article is devoted to a study of the geometry and kinematics of the Mecanum wheels, also known as Ilon wheels or the Swedish wheels. The Mecanum wheels are one of the types of omnidirectional wheels. This property is provided by peripheral rollers whose axes are deviated from the wheel one by 45 degrees. A unified approach to studying the geometry and kinematics of the Mecanum wheels on a plane and on the internal or external surface of a sphere is proposed. Kinematic relations for velocities at the contact point of the wheel and the supporting surface, and angular velocities of the roller relative to the supporting surface are derived. They are necessary to describe the dynamics of the Mecanum systems taking into account forces and moments of contact friction in the presence of slipping. From the continuous contact condition, relations determining the geometry of the wheel rollers on a plane and on the internal or external surface of a sphere are obtained. The geometric relations for the Mecanum wheel rollers could help to adjust the existing shape of the Mecanum wheel rollers of spherical robots and ballbots to improve the conditions of contact between the rollers and the spherical surface. An analytical study of the roller geometry was carried out, and equations of their generatrices were derived. Under the no-slipping condition, expressions for rotational velocities of the wheel and the contacting roller are obtained. They are necessary for analyzing the motion of systems within the framework of nonholonomic models, solving problems of controlling Mecanum systems and improving its accuracy. Using the example of a spherical robot with an internal three-wheeled Mecanum platform, the influence of the rollers on the robot movement was studied at the kinematic level. It has been established that the accuracy of the robot movement is influenced not only by the geometric parameters of the wheels and the number of rollers, but also by the relationship between the components of the platform center velocity and its angular velocity. Results of the numerical simulation of the motion of the spherical robot show a decrease in control accuracy in

A Study on the Reproduction of Traditional Chinese Painting Techniques and Their Innovation under Digital Transformation

Abstract After a long period of development and evolution, Chinese painting has formed a set of regular artistic language, which highlights the national characteristics of Chinese painting and is an important factor distinguishing it from other works of art. As the main characteristic of Chinese painting, this paper analyzes the traditional techniques of Chinese painting according to the three characteristics of Chinese painting: line, ink, and color, and analyzes the language of programed techniques. Then the method of digital reproduction of traditional techniques of Chinese painting is proposed and elaborated with the example of “The Western Garden of Elegant Gathering”. Subsequently, for the complexity of hand movement recognition of Chinese painting techniques, this paper proposes a sample splitting and re-fusion algorithm RCF based on sEMG to optimize the hand movement signals and construct a hand movement recognition model based on surface EMG signals. Through empirical analysis, the SVM classifier used in this paper has an average recognition correctness rate of 98%, which is 6% and 3% higher than BP and LDA, respectively. Meanwhile, the recognition accuracy of the hand movements of the eight techniques is all above 95%, with an average recognition accuracy of 98.5%. The mean value of all five indicators of the experience evaluation of Chinese painting techniques reproduction and innovation exceeded 4 points, and the overall average score was 4.542, indicating that the experiencers were very satisfied with the experience of Chinese painting techniques reproduction to innovation in this paper. The research of this paper provides theoretical and technical guidance for the reproduction and innovation of traditional Chinese painting techniques, as well as ideas for the organic combination of digital technology and traditional Chinese paintings.

Aerobics Arm Movement Trajectory Recognition with Motion Computer Assistance

Abstract Aerobics is an internationally famous fitness and sports competition program. This paper aims to design an aerobics arm movement trajectory recognition method with the assistance of a sports computer to detect aerobics movements in real-time. The study first introduces the Gaussian kernel function based on the Kalman filter pose solution method to construct the aerobics arm GP-SUKF pose solution model. The acceleration data are then coordinate transformed to remove the gravity component of each axis, and the features of the aerobics action trajectory are extracted by combining the time-frequency domain integration method and eliminating the cumulative error. Finally, a support vector machine algorithm based on particle swarm optimization is constructed to classify and identify the features extracted from the trajectory of the extracted aerobics arm. In the simulation experiments, the algorithm in this paper provides more motion trajectory points for the aerobics arm movements. It is closer to the actual value of the wrist movements offered by the OptiTrack system, with the error ranging from 0.02m to 0.04m, and a better tracking effect can be obtained in the case of fast movements. This study can accurately track the trajectory of aerobics arm movements and provide more accurate posture and movement assistance for professional athletes and bodybuilders.

Research on optimal path planning technology for industrial robots based on improved IDE algorithm

Abstract Realizing efficient and accurate positional movement of industrial robots is one of the key technologies to accelerate industrial development. In this paper, a four-wheel-drive McNamee mobile robot is selected as the research object, and the improved IPSO-IDE algorithm is proposed, which is combined with a PID controller to realize the accurate positioning and optimal path planning of industrial machines. The experimental results of robot kinematic parameter calibration show that the distance error of the robot calibrated with the algorithm is greatly reduced, and not only that, the IPSO-IDE algorithm reduces the average distance error of the robot to only 0.124 mm, which is much smaller than that of the PSO algorithm and the DE algorithm. The IPSO-IDE algorithm is capable of planning the optimal motion path in a short time, as demonstrated by the results of robot map environment simulation experiments. In the simulated map environment of 10 m × 10 m, the convergence speed of the algorithm’s planning path is 12.69 seconds, and the generated optimal path length is 15.0221 m, which is better than other algorithms. In conclusion, the proposed IPSO-IDE algorithm has an excellent performance in terms of path-planning capabilities for industrial robots.

KITA FIT Application: validate through the system usability scale (SUS)

The accuracy of physical movement in the world of sports and health, as well as the problems arising from a lack of understanding of the basic principles of exercise and the lack of literacy in the digital age, are issues that need to be solved. The research aims to validate, evaluate, and develop the Android-based We Fit App for the community as well as educational learning media for improving sports literacy and physical literacy in the era of digitalization. The research method uses the Likert scale and the System Usability Scale (SUS), involving the public and students. Research results show that our Fit app is successful and can be said to be worthwhile in improving digital literacy and physical literacy in society, with a high level of user satisfaction. Includes features of sports articles, brief insights, learning videos, quizzes, and reading references supporting the development of digital literacy in the world of sports The average user stated that the Kita Fit app's validity score reached 4.7, which indicates that it is worth using and effective when used. Here's a summary of the application draft results from the embroidery that has been made. The Kita Fit team developed the features and contents of the app based on the judgment of the user during the validity test, this strengthens user satisfaction with the use of the application for everyday activities.Key words: literacy, sports literation, digital literature, physical, health, the era of digitalization

Assessing the parameters of electromagnetic fields and microclimate in the radionuclide department of the positron emission tomography centre

Currently, in the Russian Federation there is an increase in the number of positron emission tomography scans. As a result, the collective dose from radionuclide diagnostics in 2021 compared to 2020 increased by 62.0%, the average effective dose per one study in positron emission tomography (PET) centres of Moscow from 2015 to 2019. increased from 1.71 to 4.41 mSv. This leads to an increase in radiation doses not only for patients, but also for medical workers, and makes it urgent to develop modern effective measures to prevent the adverse effects of ionizing radiation.
 The aim of the study is to evaluate the parameters of electromagnetic fields and microclimate in the premises of the radionuclide department of the PET centre.
 During the study, in the main working areas of the radionuclide department, the following levels were assessed: the strength levels of electric and magnetic fields of industrial frequency, relative humidity, temperature and air speed using generally accepted measures in occupational hygiene.
 zThe main parameters of electromagnetic fields and microclimate (temperature, humidity, air speed) comply with hygienic standards in industrial workplaces. At the same time, in certain working areas of the radionuclide department, higher levels of electromagnetic fields were recorded, as well as a decrease in relative humidity and air velocity.
 Monitoring the parameters of electromagnetic fields and microclimate is important for improving the working conditions of medical workers in the radionuclide department of the PET centre, which helps maintain the functional state of workers at a high level, increases concentration and accuracy of movements, increases the speed of work manipulations, and reduces the time of contact with radionuclides when administered to patients and thereby reduce the dose load on health workers.
 Ethics. The study does not require the opinion of this committee.

Brian Electrical Activity Acquisition and Decoding Algorithms For Using BMIs

Physical disabilities in limbs often lead to severe impacts on personal life and work. However, BMI technology offers hope for amputees or paralyzed individuals to regain control and motor abilities. By reading signals from the brain's motor cortex, BMI technology enables the control of robotic arms, cursors, and more, facilitating functional recovery. The key lies in identifying and extracting information related to specific tasks or intentions from brain signals, where the decoder determines the accuracy, reliability, and sensitivity of the final movements. This paper aims to provide a brief overview of the decoding steps in BMIs. The paper will discuss how the raw signals are acquired from the brain, including invasive BMI and noninvasive BMI, along with the respective advantages and disadvantages of these two approaches. Then, it will explore how these signals are processed into specific commands through both linear and nonlinear algorithms. Additionally, the paper will discuss the strengths, limitations, and feasibility of different decoding methods in human-computer interaction applications. This review offers researchers a preliminary understanding of the acquisition and decoding algorithms for brain electrical activity in BMIs. By evaluating the strengths and limitations of different algorithms, we can provide guidance for designing and improving BMI systems, thereby supporting the achievement of more accurate and efficient human-computer interaction.

Analysis of Positioning Accuracy in Case of Design Errors in the Installation of Mecanum Wheels of the Mobile Platform

Introduction. Mobile robots capable of omnidirectional movement are widely used in various fields of human activity. To provide high accuracy of positioning of omnidirectional platforms with mecanum wheels, it is required to develop their detailed mathematical models used in the construction of a motion control system. Due to the complicated design of the mecanum wheels, various errors may occur during the construction of omnidirectional platforms, including the error of installing such wheels on the platform. Its effect on the accuracy of the platform movement has not been studied before. This work aims at assessing the positioning errors that arise due to the presence of design errors in the installation of mecanum wheels, and analyzing the effect of these errors on the accuracy of program motion testing when using control at the kinematic level.Materials and Methods. The analysis of positioning accuracy was based on mathematical modeling of the platform kinematics, taking into account structural errors in the installation of mecanum wheels. To describe the relationship between the angular speeds of rotation of the wheels and the speeds of the platform, the conditions of nonslip of the contact points on the support surface were used. Numerical calculations were carried out in the Wolfram Mathematica package.Results. A formula was obtained for estimating errors in platform pseudovelocities under program control formed at the kinematic level. The estimation of the errors of the platform speeds for simple movements was carried out. According to the calculation results, it has been shown that the speed errors are significant for robots with mecanum wheels operating autonomously.Discussion and Conclusion. The calculation results demonstrated the significant impact of wheel installation errors on the positioning accuracy of the mecanum-platform, and confirmed the need to take into account these design errors when creating autonomous mecanum-platforms. The constructed model of the robot's kinematics makes it possible to predict errors in platform speeds that arise under program control, as well as deviations of the coordinates of the geometric center of the platform from the program motion. The proposed kinematic model can be used to improve the positioning accuracy through forming a platform motion control that compensates for the influence of wheel installation errors.

Evaluation of different feedback designs for target guidance in human controlled robotic cranes: A comparison between high and low performance groups

Labour shortages and costly operator training are driving the need for digital on-board robotic crane operator support in forestry and construction. This simulator study investigated the effects of sonification (auditory, pitch/loudness) and continuous visual (brightness/size) feedback on aiming movements with a robotic crane for low and high performers. The feedback was designed non-linear and linear. Thirty-six participants controlled a robotic crane bimanually using joysticks across 320 movements. Performance and skill indicators (movement time, accuracy, trajectory, smoothness) as well as satisfaction, and usefulness were assessed. Low-performing participants showed higher movement accuracy, particularly with non-linear pitch feedback compared to visual feedback. High performers exhibited no significant performance improvement in movement time, accuracy, or smoothness. There was no effect of linear or non-linear mapping of the feedback. Additionally, perceived satisfaction was lower with auditory than visual feedback. These results suggest that real-time auditory feedback can enhance operator accuracy whereas acceptance remains challenging.

ENHANCING URBAN VEHICULAR NAVIGATION: IMPROVING CLASSICAL TOPOLOGICAL MAP MATCHING THROUGH RAY-CASTING

Abstract. Navigation is of paramount importance for land vehicles as it enables efficient and accurate movement from one location to another. Whether it is for personal navigation, commercial transportation, or emergency services, reliable navigation systems play a crucial role in ensuring safety, optimizing routes, and enhancing overall operational efficiency. This paper presents the integration of classical Topological Map Matching (TMM) with the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS), addressing the limitations of relying solely on road centerlines. A novel solution is proposed, leveraging the ray-casting algorithm to determine the predicted position’s area and employing a two-stage kinematic update process for enhanced positioning accuracy. The solution’s efficacy is evaluated through tests conducted on simulated GNSS outages within a road experiment conducted in the City of Toronto, demonstrating substantial improvements compared to the classical TMM approach. Notably, the proposed method achieved a considerable 82.33% reduction in RMS positioning error and a 33.71% improvement in maximum positioning error during the longest GNSS outage. By overcoming the limitations of classical TMM algorithms, this research contributes to the advancement of navigation and tracking systems, with future work focusing on practical implementations and optimization for diverse navigation scenarios.

Scale-invariant changes in corticospinal excitability reflect multiplexed oscillations in the motor output.

In the absence of disease, humans produce smooth and accurate movement trajectories. Despite such 'macroscopic' aspect, the 'microscopic' structure of movements reveals recurrent (quasi-rhythmic) discontinuities. To date, it is unclear how the sensorimotor system contributes to the macroscopic and microscopic architecture of movement. Here, we investigated how corticospinal excitability changes in relation to microscopic fluctuations that are naturally embedded within larger macroscopic variations in motor output. Participants performed a visuomotor tracking task. In addition to the 0.25Hz modulation that is required for task fulfilment (macroscopic scale), the motor output shows tiny but systematic fluctuations at ∼2 and 8Hz (microscopic scales). We show that motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) during task performance are consistently modulated at all (time) scales. Surprisingly, MEP modulation covers a similar range at both micro- and macroscopic scales, even though the motor output differs by several orders of magnitude. Thus, corticospinal excitability finely maps the multiscale temporal patterning of the motor output, but it does so according to a principle of scale invariance. These results suggest that corticospinal excitability indexes a relatively abstract level of movement encoding that may reflect the hierarchical organisation of sensorimotor processes. KEY POINTS: Motor behaviour is organised on multiple (time)scales. Small but systematic ('microscopic') fluctuations are engrained in larger and slower ('macroscopic') variations in motor output, which are instrumental in deploying the desired motor plan. Corticospinal excitability is modulated in relation to motor fluctuations on both macroscopic and microscopic (time)scales. Corticospinal excitability obeys a principle of scale invariance, that is, it is modulated similarly at all (time)scales, possibly reflecting hierarchical mechanisms that optimise motor encoding.