Sports Applications Research Articles

A self-powered wearable body-detecting/brain-stimulating system for improving sports endurance performance

A new self-powered wearable body-detecting/brain-stimulating system for improving sports endurance performance has been realized. This wearable system can real-time monitor body vital signs and transmit neural-stimulating signal into the brain. The whole system can work without any external electricity power and be powered by the body itself. The system on the flexible substrate is integrated from power generator, body monitoring unit, data processing module and brain stimulating electrodes. A piezoelectric power generator can harvest the motion energy of athletes through converting mechanical energy into electricity. The body monitoring unit that is fabricated from flexible piezoelectric polymer can actively detect respiration, carotid artery and heartbeat. The data processing module can process physiological information and the detected information can be used to trigger the neural stimulator module for neural stimulations. Demonstration of the system with stimulating electrodes implanted in the medial forebrain bundle in running mice reveals great enhancement of endurance performance. This study provides a novel self-powered strategy for wearable brain-machine-interface system and extends its application in sports and health monitoring.

Experimental and Numerical Analysis of 3D Printed Polymer Tetra-Petal Auxetic Structures under Compression

Auxetic structures possess a negative Poisson ratio (ν < 0) as a result of their geometrical configuration, which exhibits enhanced indentation resistance, fracture toughness, and impact resistance, as well as exceptional mechanical response advantages for applications in defense, biomedical, automotive, aerospace, sports, consumer goods, and personal protective equipment sectors. With the advent of additive manufacturing, it has become possible to produce complex shapes with auxetic properties, which could not have been possible with traditional manufacturing. Three-dimensional printing enables easy and precise control of the geometry and material composition of the creation of desirable shapes, providing the opportunity to explore different geometric aspects of auxetic structures with a variety of different materials. This study investigated the geometrical and material combinations that can be jointly tailored to optimize the auxetic effects of 2D and 3D complex structures by integrating design, modelling approaches, 3D printing, and mechanical testing. The simulation-driven design methodology allowed for the identification and creation of optimum auxetic prototype samples manufactured by 3D printing with different polymer materials. Compression tests were performed to characterize the auxetic behavior of the different system configurations. The experimental investigation demonstrated a Poisson’s ration reaching a value of ν = −0.6 for certain shape and material combinations, thus providing support for preliminary finite element studies on unit cells. Finally, based on the experimental tests, 3D finite element models with elastic material formulations were generated to replicate the mechanical performance of the auxetic structures by means of simulations. The findings showed a coherent deformation behavior with experimental measurements and image analysis.

The impact of values based leadership on sport coaching

There is growing concern that the current cultural environment in sports fails to develop ethical and moral character in athletes. An examination of the development and genesis of values-based leadership (VBL) in business allows the construction of a model for VBL in sport coaching. After defining each VBL behavior a literature review documents previously confirmed benefits occurring in the sport environment. The four VBL theories of authentic, ethical, servant and transformational leadership are identified as most useful. In depth interviews and ethnographic observations are conducted with eight NCAA Division 1 basketball coaches to determine the origin, application, and priority of the values underlying individual VBL behaviors. Twenty NCAA coaches were interviewed and observed, with eight displaying behaviors consistent with the values they espoused. Based on the VBL business literature, a literature review of VBL applications in sport and the NCAA coaches studied, a VBL model for sport coaching is proposed.

Estimating Growth in Height from Limited Longitudinal Growth Data Using Full-Curves Training Dataset: A Comparison of Two Procedures of Curve Optimization-Functional Principal Component Analysis and SITAR.

A variety of models are available for the estimation of parameters of the human growth curve. Several have been widely and successfully used with longitudinal data that are reasonably complete. On the other hand, the modeling of data for a limited number of observation points is problematic and requires the interpolation of the interval between points and often an extrapolation of the growth trajectory beyond the range of empirical limits (prediction). This study tested a new approach for fitting a relatively limited number of longitudinal data using the normal variation of human empirical growth curves. First, functional principal components analysis was done for curve phase and amplitude using complete and dense data sets for a reference sample (Brno Growth Study). Subsequently, artificial curves were generated with a combination of 12 of the principal components and applied for fitting to the newly analyzed data with the Levenberg–Marquardt optimization algorithm. The approach was tested on seven 5-points/year longitudinal data samples of adolescents extracted from the reference sample. The samples differed in their distance from the mean age at peak velocity for the sample and were tested by a permutation leave-one-out approach. The results indicated the potential of this method for growth modeling as a user-friendly application for practical applications in pediatrics, auxology and youth sport.

Integrated All-Fiber Electronic Skin toward Self-Powered Sensing Sports Systems.

The development of wearable electronic skins (E-skins) requires devices with high flexibility, breathability, and antibacterial activity, as in sports sensing technology. Here, we report a flexible, breathable, and antibacterial triboelectric nanogenerator (TENG)-based E-skin for self-powered sensing in volleyball reception statistics and analytics, which is fabricated by sandwiching a silver nanowire (Ag NW) electrode between a thermoplastic polyurethane (TPU) sensing layer and a poly(vinyl alcohol)/chitosan (PVA/CS) substrate. Benefiting from an outstanding breathability of 10.32 kg m-2 day-1 and biocidal properties of CS and Ag NW, the E-skin offers excellent thermal-moisture comfort and a remarkable antibacterial effect on Escherichia coli and Staphylococcus aureus. A pressure sensitivity of 0.3086 V kPa-1 is demonstrated in the sensing range of 6.65-19.21 kPa. Besides, a volleyball reception statistical and analytical system is further developed based on a 2 × 3 E-skin array. According to this work, the integration of wearable electronic devices with self-powered sensors may expand practical applications in sports.

RETRACTED ARTICLE: Human motion capture system in sports performance based on Internet of Things technology and wireless inertial sensor

Taking human movements has very good prospects of application in sports, animated projects, medicine and health and other areas. This article aims to study the human motion capture system in sports performances based on the Internet of Things technology and wireless inertial sensors. This article first introduces the theory and characteristics of the Internet of Things and motion capture; next, according to the different characteristics of the sensors in the inertial motion capture system, a two-step Kalman filter is proposed to process the accelerometer and the magnetometer separately and, finally, the structure of this article. The human body motion model is used to analyze the acceleration dynamic error that occurs during the motion. In addition, an inertial motion capture system is constructed to obtain and visualize the structure of each motion node. The experimental results in this paper show that the Kalman filtering algorithm can ensure the accuracy of angle estimation under different motion states and has good fault tolerance to external interference. Among them, the error of the static state is reduced by 23.1%.

Polymer foams as advanced energy absorbing materials for sports applications—A review

AbstractThe use of polymer foams is becoming increasingly important due to the attainable weight reduction and value‐added properties. The development of foam structures is a popular research area as they have outstanding energy absorbing capability, which is related to the special deformation mechanisms of the cell structure (cell wall buckling and collapse of the cells). This property is exploited by the sports industry, where the main task of such products is to protect the health of the athlete and to ensure safe sports conditions. This review provides a comprehensive presentation of the advanced energy absorbing applications of polymeric foams in sports. The article presents in detail the processing technologies of polymer foams, as well as the sports‐specific regulations which contain the requirements for sports products. The impact damping of polymeric foams is typically determined by falling weight impact tests, which were used in several previous studies. However, it is a great challenge to compare the published results, as the test parameters and the tested materials are different. Currently, an unexplored field of research is the detailed study of multilayer sandwich foam structures. Understanding the effect of layer order on mechanical properties would help researchers achieve a major improvement in this field.

Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data.

Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations. We use wearable inertial sensors to obtain human table tennis action data of professional table tennis players and non-professional table tennis players, and extract the features from them. Finally, we propose a new method based on multi-dimensional feature fusion convolutional neural network and fine-grained evaluation of human table tennis actions. Realize ping-pong action recognition and evaluation, and then achieve the purpose of auxiliary training. The experimental results prove that our proposed multi-dimensional feature fusion convolutional neural network has an average recognition rate that is 0.17 and 0.16 higher than that of CNN and Inception-CNN on the nine-axis non-professional test set, which proves that we can better distinguish different human table tennis actions and have a more robust generalization performance. Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training.

Effect of loading rates on the in-plane compressive properties of additively manufactured ABS and PLA-based hexagonal honeycomb structures

Honeycomb structures find numerous applications in automotive, aerospace, sports, and other similar engineering fields. Such incorporation is made possible by the excellent crushing resistance and specific energy absorption capabilities. However, manufacturing such structures through conventional processes is highly laborious and expensive. Such a drawback can be largely mitigated by the adoption of additive manufacturing (AM) processes. Consequently, in this study, hexagonal honeycomb structures are subjected to experimental tests to determine their compressive strength under different loading rates. In addition to this, attempts have also been made to evaluate the effect of different materials and the unit cell dimensions on the compressive properties. The test specimens of different wall thicknesses are manufactured by fused deposition modelling (FDM) using PLA and ABS as the base materials. The samples are then subjected to compressive tests using a standard UTM to quantify the effect of the cell geometrical parameters and the loading rate on the overall compressive nature of the structures. The results show that the compression properties are primarily affected by the loading rate, material properties and the cell-wall thickness of the structures. The initial compressive yield stress and plateau stress generally increase up to a given value of loading rate, after which the strength decline. The cell-wall thickness of the structure influences the threshold loading rate. Therefore, this study provides a preliminary understanding of the compressive properties of AM hexagonal honeycomb structures to analyse the prospects for application in real-world engineering applications. It is proposed that such structures find profound applications in structural components of aerospace equipment, automotive parts, sports gear, and other similar areas of interest where high strength and energy absorption are of predominant importance.

Sport and fitness app uses: a review of humanities and social science perspectives

Sport and fitness mobile applications (SFMAs) have led to significant changes in how people engage in sport and physical activity. This development is part of a broader trend of self-tracking (the ‘quantified self’) and gamification, whose effects are discussed in an increasing number of publications in the humanities and social sciences. The aim of this interdisciplinary literature review is to provide an overview of the main research results on these apps. It summarises their emergence and the discourse of those who promote them, the factors leading to their adoption, their uses in practice, the reasons for their abandonment or rejection, and the risks and perverse effects linked to their use. The main sociological, psychological and philosophical interpretations of the phenomenon are also outlined: mobile applications as a tool for behaviour change, the agentive capacities of these sociotechnical systems, and the contemporary imperative of self-management. Some users find real support in SFMAs to set goals, plan their sessions, and/or to make physical activity a regular routine. However, for others, their use becomes excessive, leading to frustration or changes in social behaviour. Many studies point out the difficulty of engaging athletes in the use of SFMAs in a lasting way. While instances of long-term and balanced use are not uncommon, they coexist with use that borders on obsession, or even forms of dependence. We conclude with current research priorities and highlight avenues of research that merit further study.

Interpretation of Gait Supervising Mechanism Using Sensor Integrated Makeshift and Analysing Pattern by K-Means Clustering Algorithm

Human mobility or walking pattern(gait) is described as the interpreter movements of the rotatory body to achieve extensive range of locomotion. Gait analysis is foremost widely used technique for identifying abnormalities in the lower extremities and gait characteristics essentially support HAT (Head, Arm & Trunk). The act of walking is unconscious when there are no dysfunctions, but for ambulated the continuous monitoring is required. The existing clinical analysis method couldn’t achieve the daily walking routine within the confinement of a room.The proposed method focuses on developing an ambulatory system on daily routines by incorporating feasible techniques for achieving the gait pattern which is not confined to a room atmosphere where all possibilities of walking pattern can’t be reached.This system has expounded an ideology, to interpret the gait parameters using an insole type shoe integrated sensor system. Here, a wearable gait system which is incorporated with force resistive sensors, piezo sensors, inertial sensors and IR sensors are interfaced to the ESP 32. The corresponding sensors extract the data of kinematic angles, kinetics, foot pressure, step count and foot stride investigations.The system proved to be efficient in finding the phases and orientation of the individual by interpreting values from the device. Acquired data can be clustered together to find the abnormal and normal values by applying K-Means clustering algorithm, later the values are utilized in biomechanics for rectifying posture or movement related problems.The device will have several applications in sports, rehabilitation medicine and post-surgery treatment.

Study on the Theoretical System Construction and Role of Physical Education Based on Sports Social Psychology

Objectives: With the popularity of the national fitness program, sports theory has gradually become the focus of academic research. However, because of the behavior of the object and the complexity of the psychological, it is difficult to build a relatively perfect theoretical system. Based on this, in this paper, the theoretical application of social psychology in sports and the construction principles of sports theory system under sports social psychology were analyzed in detail. Methods: In order to verify the practical application of the theoretical system of sports in social psychology, physical education teaching system was regarded as a practical case. From the perspective of social psychology, role theory, physical education in the conflict and the root causes of physical education and psychological interaction and other content were studied, Results: the results show that the traditional sports teaching theory is not suitable for the development of the current students, and the new PE teaching system from the perspective of social psychology can better tap the potential of students. Conclusion: To sum up, the research in this paper can promote the development of sports in China and provide theoretical and technical support for physical education.

Auxetic structure metamaterial for crash safety of sports helmet

In this paper, we talk about the use of Auxetics in Sports Helmets, orientation, and the type of auxetic used. It also covers how the newly designed auxetic structures can sustain more impact energy when compared to the existing auxetic structures. This comparison gives us more clarity about the uses of auxetics in various other applications. Auxetics in simple terms are structures that have a negative Poisson's ratio. This is just a structural property and does not depend upon the material. Hence, it can be used in so many different applications such as defense applications, sports applications, impact absorption, drug dispensers when pores are open, test bay containment, etc. On moving forward, modifying a structure showed that there are improvements concerning Poisson's ratio as well as impact resistance. The challenge was then to make the foam such that it can absorb maximum impact energy. Based on this hypothesis, the proposed design was tested against an existing structure to postulate the difference in the impact energy. The Poisson’s ratio obtained was a staggering improvement from −0.73 to −1.27 which shows 75% decrease in Poisson’s ratio while boasting an increase in the overall energy of 330% from 50 J to 165 J.

Recent Progress in Bionic Skin Based on Conductive Polymer Gels.

Bionic skin sensors based on conductive polymer gels have garnered interest for their potential applications in human-computer interaction, soft robotics, biomedical systems, sports, and healthcare, because of their intrinsic flexibility and stretchability embedded at the material level, and other such as self-healing, adhesion, high, and low temperature tolerance properties that can be tuned through macromolecular design. Here, important advances in polymer gel-based flexible sensors over recent years are summarized, from material design, sensor fabrication to system-level applications. This review focuses on the representative strategies of design and preparing of conductive polymer gels, and adjusting their conductivity, mechanics, and other properties such as self-healing and adhesiveness by controlling the macromolecular network structures. The state-of-art of present flexible pressure and strain sensors, temperature sensors, position sensors, and multifunctional sensors based on capacitance, voltage, and resistance sensing technologies, are also systematically reviewed. Finally, perspectives on issues regarding further advances and challenges are provided.

Activity Tracking Using Ear-Level Accelerometers.

Introduction: By means of adding more sensor technology, modern hearing aids (HAs) strive to become better, more personalized, and self-adaptive devices that can handle environmental changes and cope with the day-to-day fitness of the users. The latest HA technology available in the market already combines sound analysis with motion activity classification based on accelerometers to adjust settings. While there is a lot of research in activity tracking using accelerometers in sports applications and consumer electronics, there is not yet much in hearing research.Objective: This study investigates the feasibility of activity tracking with ear-level accelerometers and how it compares to waist-mounted accelerometers, which is a more common measurement location.Method: The activity classification methods in this study are based on supervised learning. The experimental set up consisted of 21 subjects, equipped with two XSens MTw Awinda at ear-level and one at waist-level, performing nine different activities.Results: The highest accuracy on our experimental data as obtained with the combination of Bagging and Classification tree techniques. The total accuracy over all activities and users was 84% (ear-level), 90% (waist-level), and 91% (ear-level + waist-level). Most prominently, the classes, namely, standing, jogging, laying (on one side), laying (face-down), and walking all have an accuracy of above 90%. Furthermore, estimated ear-level step-detection accuracy was 95% in walking and 90% in jogging.Conclusion: It is demonstrated that several activities can be classified, using ear-level accelerometers, with an accuracy that is on par with waist-level. It is indicated that step-detection accuracy is comparable to a high-performance wrist device. These findings are encouraging for the development of activity applications in hearing healthcare.

Comparing Human Activity Recognition Models Based on Complexity and Resource Usage

Human Activity Recognition (HAR) is a field with many contrasting application domains, from medical applications to ambient assisted living and sports applications. With ever-changing use cases and devices also comes a need for newer and better HAR approaches. Machine learning has long been one of the predominant techniques to recognize activities from extracted features. With the advent of deep learning techniques that push state of the art results in many different domains like natural language processing or computer vision, researchers have also started to build deep neural nets for HAR. With this increase in complexity, there also comes a necessity to compare the newer approaches to the previous state of the art algorithms. Not everything that is new is also better. Therefore, this paper aims to compare typical machine learning models like a Random Forest (RF) or a Support Vector Machine (SVM) to two commonly used deep neural net architectures, Convolutional Neural Nets (CNNs) and Recurrent Neural Nets (RNNs). Not only in regards to performance but also in regards to the complexity of the models. We measure complexity as the memory consumption, the mean prediction time and the number of trainable parameters of the models. To achieve comparable results, the models are all tested on the same publicly available dataset, the UCI HAR Smartphone dataset. With this combination of prediction performance and model complexity, we look for the models achieving the best possible performance/complexity tradeoff and therefore being the most favourable to be used in an application. According to our findings, the best model for a strictly memory limited use case is the Random Forest with an F1-Score of 88.34%, memory consumption of only 0.1 MB and mean prediction time of 0.22 ms. The overall best model in terms of complexity and performance is the SVM with a linear kernel with an F1-Score of 95.62%, memory consumption of 2 MB and a mean prediction time of 0.47 ms. The two deep neural nets are on par in terms of performance, but their increased complexity makes them less favourable to be used.

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise.

Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review is to analyze the literature related to the application of HPE in SPE, the available data, methods, performance, opportunities, and challenges. One reviewer applied different inclusion and exclusion criteria, as well as quality metrics, to perform the paper filtering through the paper databases. The Association for Computing Machinery Digital Library, Web of Science, and dblp included more than 500 related papers after the initial filtering, finally resulting in 20. In addition, research was carried out regarding the publicly available data related to this topic. It can be concluded that even if related public data can be found, much more data is needed to be able to obtain good performance in different contexts. In relation with the methods of the authors, the use of general purpose systems as base, such as Openpose, combined with other methods and adaptations to the specific use case can be found. Finally, the limitations, opportunities, and challenges are presented.

A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino.

Indirect Estimation of Breathing Rate from Heart Rate Monitoring System during Running.

Recent advances in wearable technologies integrating multi-modal sensors have enabled the in-field monitoring of several physiological metrics. In sport applications, wearable devices have been widely used to improve performance while minimizing the risk of injuries and illness. The objective of this project is to estimate breathing rate (BR) from respiratory sinus arrhythmia (RSA) using heart rate (HR) recorded with a chest belt during physical activities, yielding additional physiological insight without the need of an additional sensor. Thirty-one healthy adults performed a run at increasing speed until exhaustion on an instrumented treadmill. RR intervals were measured using the Polar H10 HR monitoring system attached to a chest belt. A metabolic measurement system was used as a reference to evaluate the accuracy of the BR estimation. The evaluation of the algorithms consisted of exploring two pre-processing methods (band-pass filters and relative RR intervals transformation) with different instantaneous frequency tracking algorithms (short-term Fourier transform, single frequency tracking, harmonic frequency tracking and peak detection). The two most accurate BR estimations were achieved by combining band-pass filters with short-term Fourier transform, and relative RR intervals transformation with harmonic frequency tracking, showing 5.5% and 7.6% errors, respectively. These two methods were found to provide reasonably accurate BR estimation over a wide range of breathing frequency. Future challenges consist in applying/validating our approaches during in-field endurance running in the context of fatigue assessment.

A Motion Capturing and Energy Harvesting Hybridized Lower-Limb System for Rehabilitation and Sports Applications.

Lower‐limb motion monitoring is highly desired in various application scenarios ranging from rehabilitation to sports training. However, there still lacks a cost‐effective, energy‐saving, and computational complexity‐reducing solution for this specific demand. Here, a motion capturing and energy harvesting hybridized lower‐limb (MC‐EH‐HL) system with 3D printing is demonstrated. It enables low‐frequency biomechanical energy harvesting with a sliding block‐rail piezoelectric generator (S‐PEG) and lower‐limb motion sensing with a ratchet‐based triboelectric nanogenerator (R‐TENG). A unique S‐PEG is proposed with particularly designed mechanical structures to convert lower‐limb 3D motion into 1D linear sliding on the rail. On the one hand, high output power is achieved with the S‐PEG working at a very low frequency, which realizes self‐sustainable systems for wireless sensing under the Internet of Things framework. On the other hand, the R‐TENG gives rise to digitalized triboelectric output, matching the rotation angles to the pulse numbers. Additional physical parameters can be estimated to enrich the sensory dimension. Accordingly, demonstrative rehabilitation, human‐machine interfacing in virtual reality, and sports monitoring are presented. This developed hybridized system exhibits an economic and energy‐efficient solution to support the need for lower‐limb motion tracking in various scenarios, paving the way for self‐sustainable multidimensional motion tracking systems in near future.