Stroke Trajectory Research Articles

Robust gesture recognition method toward intelligent environment using Wi-Fi signals

Wi-Fi-based gesture recognition represents an emerging paradigm of human–computer interaction. While deep learning-based and model-based solutions are crucial for improving accuracy and generalization performance, they often depend heavily on extensive datasets and the widespread deployment of sensing devices. In this paper, we address the challenge involving limited sensing data and devices. We propose a signal-based gesture recognition method that leverages gesture coding based on stroke trajectory and feature representation based on dynamic phase changes. Specifically, we develop an endpoint detection algorithm to ensure precise gesture recognition. Additionally, a subcarrier selection algorithm is designed to select optimal subcarriers, capturing comprehensive gesture information. Extensive experiments are conducted to evaluate the performance. Results demonstrate an average accuracy of 93.67% for six gestures. This approach effectively mitigates the impact of location and environment factors on gesture characteristics, and reduces dependence on large quantity of samples and transceiver devices, obviating the need for model training.

A novel mathematical model of the badminton smash: simulation and modeling in biomechanics

Applied physics and computer methods in biomechanics have been extensively used in sports science research, including performance and biomechanics analysis. The Brachistochrone problem, which expresses the curve that an object draws quickly under gravitational forces in a vertical position, is one of the most widely used studies in classical mechanics. A similar problem arises when a badminton player intends to hit a smash with the shortest shot time. This paper aims to determine the optimal stroke trajectory for a shuttlecock smash in the shortest time. We simulate the badminton smash movement using a computer program after analyzing the shuttlecock smash analytically and numerically for several conditions. The modeling results show that a cycloid trajectory allows badminton players to smash the shuttlecock in the shortest time. Based on the experimental findings of Tsai, Huang, and Jih’s study and our models, the ratio of clear speed to smash speed is 0.75, which is still in the range of 0.71 to 0.76, and we find that a cycloid trajectory gives the shortest shuttlecock smash time. We concluded that the experimental data from this study’s literature supported our model. The novelty of this study is that we found the first powerful model and simulation of conventional Brachistochrone in the case of a badminton smash of badminton players. For badminton coaches and players, this model formulation is intended as a reference for optimizing shuttlecock shots. Furthermore, another novelty of this research is that it may lead to software that can be used to analyze the muscle strength of badminton players based on their cycloid hand trajectory and shuttlecock speed.

Fractional Differential Equations in Sports Training in Universities

Abstract The article theoretically analyzes the relevant knowledge of shot put in the shot put sports training of colleges and universities. The fractional differential equations are used to analyze the influence of the initial speed and the shot situation on the performance of the shot put. We obtain the joint angles of each stage of the shot throw through experimental design, time-consuming, final speed, limb displacement, shot-put shot speed, height, angle, and other kinematic parameters, and the shot-put motion trajectory of the picture stroke of the relevant action characteristics.

Association between the Systolic Blood Pressure Trajectory and Risk of Stroke in a Health-Management Population in Jiaozuo, China.

The trajectories of systolic blood pressure (SBP) in a screening population in Jiaozuo were examined, and the association between the different types of SBP trajectories and the risk of stroke was evaluated. Data of a fixed cohort population from the Jiaozuo Stroke Prevention and Control Project Management Special Database System that underwent community screening in 2015, 2017, 2019, and 2021 were collected. Ultimately, a total of 1,451 participants who met the inclusion criteria for this study were included in the analysis, which was performed using group trajectory modeling. The baseline SBP for each trajectory subgroup was characterized at follow-up. Kaplan-Meier analysis for each trajectory group was also performed, and the relationship between the SBP trajectory and risk of stroke onset during follow-up was validated using a Cox proportional hazards model. Based on the SBP from 2015 to 2021, this cohort population was divided into three groups based on the trajectory development patterns: the low-stable group (37.6%), the moderate-increasing group (53.4%), and the high-acutely increasing group (9%). Gender, age, body mass index, diastolic blood pressure, and fasting blood glucose level were predictive factors for the SBP trajectory group. The cumulative survival risk in the high-acutely increasing group was higher than that of the other two groups. After adjusting for potential confounding factors and using the low-stable group as a reference, the hazard ratios (95% confidence interval) for the risk of stroke onset in the moderate-increasing and high-acutely increasing groups were 1.38 (0.91-2.07) and 1.51 (0.82-2.76), respectively. The results of the analysis demonstrate that higher blood pressure trajectories are associated with a higher risk of stroke and that the risk of stroke can be reduced by better control and management of the SBP.

Air Pollution Associated With Incident Stroke, Poststroke Cardiovascular Events, and Death: A Trajectory Analysis of a Prospective Cohort.

Ambient air pollution has been widely linked with morbidity and mortality of stroke. However, its effects on dynamic progression trajectory of stroke remain unknown.We investigated the effects of ambient air pollution on progression trajectory from healthy status to incident stroke, poststroke cardiovascular diseases, and subsequent death. We retrieved 318,752 participants from the UK Biobank. The annual concentrations of air pollution [particulate matter (PM2.5, PMcoarse, PM10 and PM2.5 absorbance), nitrogen dioxide (NO2), and nitrogen oxides (NOx)] were estimated through land use regressions. A multistate regression model was used to investigate the effects of air pollution on each stage of the progression of stroke. During 3,765,630 person-years of follow-up, we identified 5,967 incident stroke patients, 2,985 poststroke cardiovascular patients, and 1,020 deaths afterward. Each 5 μg/m3 increase in PM2.5, NO2, and NOx was associated with the transition from healthy status to incident stroke [hazard ratios (HRs) (95% confidence intervals (CI)) = 1.24 (1.10, 1.40); 1.02 (1.00, 1.03); 1.01 (1.00, 1.02)] and transitions from healthy status to death directly [1.30 (1.21, 1.40); 1.03 (1.02, 1.04); and 1.02 (1.01, 1.02)]. We also observed positive associations for poststroke CVDs, especially for NO2 [1.04 (95% CI: 1.02, 1.06)], but these effects gradually declined for mortality outcome. This study provides the first evidence that ambient air pollution is one important factor associated with the progression of stroke, and the effects differed across different clinical stages. A better understanding of the differential effects of air pollutants on different stroke transition stages could provide valuable insight toward targets for health management and clinical prevention.

A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

The ability of robots to write Chinese strokes, which is recognized as a sophisticated task, involves complicated kinematic control algorithms. The conventional approaches for robotic writing of Chinese strokes often suffer from limited font generation methods, which limits the ability of robots to perform high-quality writing. This article instead proposes a developmental evolutionary learning framework that enables a robot to learn to write fundamental Chinese strokes. The framework first considers the learning process of robotic writing as an evolutionary easy-to-difficult procedure. Then, a developmental learning mechanism called “Lift-constraint, act and saturate” that stems from developmental robotics is used to determine how the robot learns tasks ranging from simple to difficult by building on the learning results from the easy tasks. The developmental constraints, which include altitude adjustments, number of mutation points, and stroke trajectory points, determine the learning complexity of robot writing. The developmental algorithm divides the evolutionary procedure into three developmental learning stages. In each stage, the stroke trajectory points gradually increase, while the number of mutation points and adjustment altitudes gradually decrease, allowing the learning difficulties involved in these three stages to be categorized as easy, medium, and difficult. Our robot starts with an easy learning task and then gradually progresses to the medium and difficult tasks. Under various developmental constraint setups in each stage, the robot applies an evolutionary algorithm to handle the basic shapes of the Chinese strokes and eventually acquires the ability to write with good quality. The experimental results demonstrate that the proposed framework allows a calligraphic robot to gradually learn to write five fundamental Chinese strokes and also reveals a developmental pattern similar to that of humans. Compared to an evolutionary algorithm without the developmental mechanism, the proposed framework achieves good writing quality more rapidly.

Transformation Invariant Pashto Handwritten Text Classification and Prediction

The use of handwritten recognition tools has increased yearly in various commercialized fields. Due to this, handwritten classification, recognition, and detection have become an exciting research subject for many scholars. Different techniques have been provided to improve character recognition accuracy while reducing time for languages like English, Arabic, Chinese and European languages. The local or regional languages need to consider for research to increase the scope of handwritten recognition tools to the global level. This paper presents a machine learning-based technique that provides an accurate, robust, and fast solution for handwritten Pashto text classification and recognition. Pashto belongs to cursive script division, which has numerous challenges to classify and recognize. The first challenge during this research is developing efficient and full-fledged datasets. The efficient recognition or prediction of Pashto handwritten text is impossible by using ordinary feature extraction due to natural transformations and handwriting variations. We propose some useful invariant features extracting techniques for handwritten Pashto text, i.e., radial, orthographic grid, perspective projection grid, retina, the slope of word trajectories, and cosine angles of tangent lines. During the dataset creation, salt and pepper noise was generated, which was removed using the statistical filter. Another challenge to face was the invalid disconnected handwritten stroke trajectory of words. We also proposed a technique to minimize the problem of disconnection of word trajectory. The proposed approach uses a linear support vector machine (SVM) and RBF-based SVM for classification and recognition.

Fractional Differential Equations in Sports Training in Universities

Abstract The article theoretically analyzes the relevant knowledge of shot put in the shot put sports training of colleges and universities. The fractional differential equations are used to analyze the influence of the initial speed and the shot situation on the performance of the shot put. We obtain the joint angles of each stage of the shot throw through experimental design, time-consuming, final speed, limb displacement, shot-put shot speed, height, angle, and other kinematic parameters, and the shot-put motion trajectory of the picture stroke of the relevant action characteristics.

Stroke recovery phenotyping through network trajectory approaches and graph neural networks

Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which can present challenges for multivariate regression approaches. This has hindered stroke researchers’ ability to achieve an integrated picture of the complex time-evolving interactions among symptoms. Here, we use tools from network science and machine learning that are particularly well-suited to extracting underlying patterns in such data, and may assist in prediction of recovery patterns. To demonstrate the utility of this approach, we analyzed data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns for 11 different neurological domains at 5 discrete time points. Our analysis identified 3 distinct stroke trajectory profiles that align with clinically relevant stroke syndromes, characterized both by distinct clusters of symptoms, as well as differing degrees of symptom severity. We then validated our approach using graph neural networks to determine how well our model performed predictively for stratifying patients into these trajectory profiles at early vs. later time points post-stroke. We demonstrate that trajectory profile clustering is an effective method for identifying clinically relevant recovery subtypes in multidimensional longitudinal datasets, and for early prediction of symptom progression subtypes in individual patients. This paper is the first work introducing network trajectory approaches for stroke recovery phenotyping, and is aimed at enhancing the translation of such novel computational approaches for practical clinical application.

Trajectory Tracking Method of Volleyball Player’s Arm Hitting Image Based on D-P Algorithm

Aiming at the problems of poor image tracking effect, low precision, and long time in the process of image tracking of volleyball player’s arm hitting, a volleyball player's arm hitting image tracking method based on D-P algorithm is proposed. This paper analyzes the basic concept, basic principle, and basic equation of D-P algorithm and collects the arm stroke trajectory image of volleyball players under the three-dimensional visual model. Using wavelet multiscale decomposition method, the arm stroke trajectory of volleyball players is filtered, and the edge contour feature points of the arm stroke image of volleyball players are extracted. Using the gray histogram feature extraction method, the gray information of volleyball player's arm hitting trajectory image is enhanced. Combined with pixel adaptive enhancement technology, the key action feature points of volleyball player's arm hitting image trajectory are located. Based on D-P algorithm, the volleyball player's arm hitting image trajectory is adjusted and modified to realize the correct tracking of volleyball player's arm hitting image trajectory. The experimental results show that the trajectory tracking effect of volleyball player's arm hitting image is better, which can effectively improve the tracking accuracy and shorten the tracking time.

Adaptation and learning processes of stroke survivors and family carers: a scoping review

Abstract Background Knowledge on the processes of adaptation and learning after stroke are scarce, hindering the development of evidence-based public health strategies to promote survivors and family carers' health and wellbeing, across the post stroke trajectory. This study aims to assess the available evidence on the processes of adaptation and learning after stroke, by mapping the main barriers and enablers according to the perspectives of stroke survivors and family carers. Methods A scoping review was performed, following PRISMA-ScR guidelines. The electronic databases PubMed, ISI Web of Science, PsycINFO and SciELO were searched for empirical, peer-reviewed, original, and full-length studies on the processes of adaptation and learning of stroke survivors and family carers, in March 2021. Eligibility and data extraction were conducted by two independent researchers. The main qualitative data were explored by thematic content analysis and quantitative findings were synthesized. Results The included studies, 29 qualitative and 1 quantitative, were published between 1994 and 2019. Most of the studies were carried out with small samples and without a specific focus on the adaptation and learning processes after stroke. Barriers and enablers to stroke adaptation and learning processes were influenced by physical, psychological and social characteristics. The poor physical and functional status of survivors, the inability to maintaining the Activities of Daily Living, emotional disturbances, lack of support and information, and changes in roles, were identify as main barriers to stroke adaptation. Using coping strategies and having psychological, educational, and financial support were described as enablers. Conclusions Public health policies and practices should consider the physical, psychological and social barriers and enablers to the stroke adaptation and learning processes to ensure a high-quality long-term care centred on survivors and family carers. Key messages Robust theoretical and methodological studies, specifically designed to deeply explore and describe the post stroke adaptation and learning processes, are needed. Understand the main barriers and enablers to adaptation and learning after stroke may be useful for developing health education interventions centred on survivors and carers preferences and needs.

CLINICAL AND ORGANIZATIONAL QUALITY INDICATORS IN THE ACUTE PHASE FOR OPTIMAL STROKE MANAGEMENT IN ADULTS: A SCOPING REVIEW

<h3>BACKGROUND/PURPOSE</h3> Optimal interdisciplinary care and services for stroke patients and their caregivers are critical factors in recovery and quality of life following the initial event. In order to measure current practices in organizations, several indicators have been developed and are used to monitor quality of care for people who had a stroke. However, few studies have been conducted on the identification and the measurement of these indicators in the stroke acute phase. The purpose of this scoping review is to identify clinical and organizational indicators relevant to monitor quality of post-stroke care during this important phase for patients, family caregivers, health professionals and managers. <h3>METHODS/RESULTS</h3> A scoping review based on the 6 steps of Arskey and O'Malley methodological framework was conducted in electronic databases (Medline, CINHAL, EBSCO, Cochrane Library, Google Scholar and Google). Grey literature and all scientific studies conducted and published between 2015 and 2021, in French and English, have been selected based on inclusion criteria. A second independent reviewer participated in study selection and data extraction. All identified indicators have been classified to measure concepts of care performance (accessibility, equity of access, efficiency, safety, patient and family responsiveness, continuity of care, efficiency and viability). A final list of indicators will be revised by a panel of clinical and scientific experts using a Delphi process. These experts will give their opinion on the relevance, validity, reliability, feasibility, usability and comparability of the identified indicators. A total of 3562 references has been identified. Of these, 25 were included in the review, but only 8 of them are research studies. A total of 242 indicators have been identified. Most of them are from grey literature and guidelines. Majority of the indicators have been classified according to the concept of safety and relate to the management of stroke complications. Few indicators relate to the concepts of equity of care and patient responsiveness. <h3>CONCLUSION/IMPLICATIONS FOR PRACTICE</h3> This scoping review highlights clinical and organizational indicators that should be monitored to improve stroke management and clinical practice in the acute phase. It also justifies the need to conduct further studies to improve the measurement of quality indicators in clinical practice during this key phase of the stroke trajectory.

Interactive liquid splash modeling by user sketches

Splashing is one of the most fascinating liquid phenomena in the real world and it is favored by artists to create stunning visual effects, both statically and dynamically. Unfortunately, the generation of complex and specialized liquid splashes is a challenging task and often requires considerable time and effort. In this paper, we present a novel system that synthesizes realistic liquid splashes from simple user sketch input. Our system adopts a conditional generative adversarial network (cGAN) trained with physics-based simulation data to produce raw liquid splash models from input sketches, and then applies model refinement processes to further improve their small-scale details. The system considers not only the trajectory of every user stroke, but also its speed, which makes the splash model simulation-ready with its underlying 3D flow. Compared with simulation-based modeling techniques through trials and errors, our system offers flexibility, convenience and intuition in liquid splash design and editing. We evaluate the usability and the efficiency of our system in an immersive virtual reality environment. Thanks to this system, an amateur user can now generate a variety of realistic liquid splashes in just a few minutes.

An LSTM Based Generative Adversarial Architecture for Robotic Calligraphy Learning System

Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data may cause the time wasting for researchers. This paper proposes a machine calligraphy learning system using a Long Short-Term Memory (LSTM) network and a generative adversarial network (GAN), which enables the robots to learn and generate the sequences of Chinese character stroke (i.e., writing trajectory). In order to reduce the size of the training set, a generative adversarial architecture combining an LSTM network and a discrimination network is established for a robotic manipulator to learn the Chinese calligraphy regarding its strokes. In particular, this learning system converts Chinese character stroke image into the trajectory sequences in the absence of the stroke trajectory writing sequence information. Due to its powerful learning ability in handling motion sequences, the LSTM network is used to explore the trajectory point writing sequences. Each generation process of the generative adversarial architecture contains a number of loops of LSTM. In each loop, the robot continues to write by following a new trajectory point, which is generated by LSTM according to the previously written strokes. The written stroke in an image format is taken as input to the next loop of the LSTM network until the complete stroke is finally written. Then, the final output of the LSTM network is evaluated by the discriminative network. In addition, a policy gradient algorithm based on reinforcement learning is employed to aid the robot to find the best policy. The experimental results show that the proposed learning system can effectively produce a variety of high-quality Chinese stroke writing.

The Experiences of Couples Affected by Stroke and Nurses Managing Patient Rehabilitation: A Descriptive Study in Singapore.

Stroke in a family affects both patients and their spousal caregivers. Despite advances in the medical management of stroke, less is known about the social and cultural factors that impact couples regarding stroke recovery. The purpose of this study was to explore the experiences of stroke from the perspectives of couples affected by stroke and the nurses managing patient rehabilitation. An interpretive descriptive study was conducted. Purposive sampling was used to enroll 17 participants, comprising eight nurses, five spousal caregivers, and four stroke survivors. Individual, in-depth interviews were performed at a rehabilitation hospital in Singapore in June 2018. The primary theme was the diverse meanings of stroke recovery attributed to limited conversations about the care decisions made by couples and rehabilitation nurses. The second theme was the challenges in nursing responsibilities that hindered the recovery of patients with stroke. The meaning of recovery differs between patients and their informal and formal care providers. This issue should be explored in patient-provider conversations, as these conversations highlight the values and preferences that affect the stroke recovery trajectory. Enhancing shared decision making by patients, spousal caregivers, and healthcare providers during the stroke trajectory may promote the alignment of values that are critical to the stroke recovery experience. Further research into whether and how to incorporate shared decision making in rehabilitation hospital settings as an interventional component is warranted to better support stroke survivors before discharge.

Stroke Kinematics Analysis and Hydrodynamic Modeling of a Buoyancy-Supported Water Strider Robot

The locomotive ability of a water strider that generates an ellipse-like spatial stroke trajectory when moving is proved to improve its moving efficiency and is seldom imitated by water strider robots. A hydrodynamic analysis can predict the performance of the robots, but research in this area is not enough. A buoyancy-supported water strider robot with the ability of producing an ellipse-like spatial stroke trajectory was designed and analyzed in this study. A semiempirical hydrodynamic model suitable for the buoyancy-supported robot was proposed to study the hydrodynamic characteristics, based on the Newton’s second law of motion. The speed, power and efficiency of the robot were analyzed on the basis of the model which confirmed that the robot performing an ellipse-like spatial stroke trajectory was conducive to its moving efficiency. The experimental results verified that the stroke mechanism produced an ellipse-like spatial trajectory in a practical application and that the robot had good mobility. The proposed hydrodynamic model was validated by comparing the theoretical speed of the robot to the experimental results.

Flatness-based Trajectory-tracking Control of Dielectric Elastomer Actuators

In recent years, actuators based on dielectric elastomers have become popular for both research and industrial applications. Due to their nonlinear behavior, they are typically used in on/off actuation. Established feedback control methods such as PID provide an effective and robust way to drive these actuators under constant setpoint requirements. However, such control methods exhibit poor trajectory tracking performance. This shortcoming is addressed in this work with a flatness-based nonlinear control design for a circular membrane actuator based on dielectric elastomers. By exploiting a nonlinear electro-mechanical model of the device, a tracking control law is designed in both feed-forward and feedback form. Moreover, it is shown how the flatness-based feed-forward control may be used to extend a conventional PID control to improve its tracking performance. The presented methods are validated and compared experimentally with a real actuator prototype. Tracking accuracy better than 10µm along a 1 mm stroke trajectory (i. e. less than 1%) is shown. This result is a significant improvement over existing PID control laws.

Shrubbery-shell inspired 3D model stylization

We propose a new method to automatically generate a shrub-branch-style shell (also called a hollow-branch sculpture) decorative pattern for an arbitrary 3D model along its surface curvature. Our method generates the branching pattern by using a particle-based model without requiring any basic tree-shape elements to be extracted from existing 2D images or 3D models. We modify the particle method by constraining the direction vectors for particle motion onto a given mesh surface so the branch structure grows along the surface to form a shell of the input object. The global and local branch shapes are changed by adjusting the growth space and growth factors, providing better controllability and diversity of the resulting patterns. In addition, the growth space for the branching pattern can be interactively assigned by a user’s brush to an arbitrary area of the mesh surface where the trajectory of the brush stroke is reflected in the branch-growth directions. The following control options are also considered: merging multiple branch clusters having different root locations, biasing the generation of strong main branches, and creating a windowed growth space for thin shapes. The versatility and effectiveness of our approach are demonstrated by generating various arborsculpture-inspired shapes.

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Conventional Chinese calligraphy robots often suffer from the limited sizes of predefined font databases, which prevent the robots from writing new characters. This paper presents a robotic handwriting system to address such limitations, which extracts Chinese characters from textbooks and uses a robot's manipulator to write the characters in a different style. The key technologies of the proposed approach include the following: 1) automatically decomposing Chinese characters into strokes using Harris corner detection technology and 2) matching the decomposed strokes to robotic writing trajectories learned from human gestures. Briefly, the system first decomposes a given Chinese character into a set of strokes and obtains the stroke trajectory writing ability by following the gestures performed by a human demonstrator. Then, it applies a stroke classification method that recognizes the decomposed strokes as robotic writing trajectories. Finally, the robot arm is driven to follow the trajectories and thus write the Chinese character. Seven common Chinese characters have been used in an experiment for system validation and evaluation. The experimental results demonstrate the power of the proposed system, given that the robot successfully wrote all the testing characters in the given Chinese calligraphic style.

Tennis Player Training Support System based on Sport Vision

Sports vision based tennis player training support system is proposed. In sports, gaze, dynamic visual acuity, eye movement and viewing place are important. In sports vision, Static eyesight, Dynamic visual acuity, Contrast sensitivity, Eye movement, Deep vision, Instant vision, Cooperative action of eye, hand and foot, and Peripheral field are have to be treated. In particular for the tennis, all of the items are very important. Furthermore, trajectory of gaze location and tennis racket stroke gives some instructions for skill-up of tennis play. Therefore, sports vision based tennis player training system is proposed. Through experiment, it is found that the proposed system does work well for improvement of tennis players’ skills.