Search Results (38)

This article presents a comprehensive analysis of the potential for microgeneration of electrical energy from human physical activity and reviews current commercial and research solutions, including stationary bicycles, treadmills, rowing ergometers, strength equipment, and kinetic floor systems. The physiological foundations of human energy generation are examined, with attention to key factors such as age, gender, fitness level, maximum oxygen uptake, heart rate, and hydration. The study includes mathematical models of energy conversion from metabolic to electrical output, incorporating fatigue as a limiting factor in long-duration performance. Available energy storage technologies (e.g., lithium-ion batteries, supercapacitors, and flywheels) and intelligent energy management systems (EMS) for use in sports facilities and net-zero energy buildings are also reviewed. As part of the study, a conceptual design of a multifunctional training and diagnostic device is proposed to illustrate potential technological directions. This device integrates microgeneration with dynamic physiological monitoring and adaptive load control through power electronic conversion. The paper highlights both the opportunities and limitations of harvesting human-generated energy and outlines future directions for sustainable energy applications in fitness environments. A preliminary economic analysis is also included, showing that while the energy payback alone is limited, the device offers commercial potential when combined with diagnostic and smart fitness services and may contribute to broader building energy efficiency strategies through integration with intelligent energy systems. Full article

In the evolving landscape of Internet of Things (IoT) applications, human activity recognition plays an important role in domains such as health monitoring, elderly care, sports training, and smart environments. However, current approaches face significant challenges: sensor data are often noisy and variable, leading to difficulties in reliable feature extraction and accurate activity identification; furthermore, ensuring data integrity and user privacy remains an ongoing concern in real-world deployments. To address these challenges, we propose a novel framework that synergizes advanced statistical signal processing with state-of-the-art machine learning and deep learning models. Our approach begins with a rigorous preprocessing pipeline—encompassing filtering and normalization—to enhance data quality, followed by the application of probability density functions and key statistical measures to capture intrinsic sensor characteristics. We then employ a hybrid modeling strategy combining traditional methods (SVM, Decision Tree, and Random Forest) and deep learning architectures (CNN, LSTM, Transformer, Swin Transformer, and TransUNet) to achieve high recognition accuracy and robustness. Additionally, our framework incorporates IoT security measures designed to safeguard data integrity and privacy, marking a significant advancement over existing methods in both efficiency and effectiveness. Full article

With the rapid development of sports technology, smart wearable devices play a crucial role in athletic training and health management. Sports fatigue is a key factor affecting athletic performance. Using smart wearable devices to detect the onset of fatigue can optimize training, prevent excessive fatigue and resultant injury, and increase efficiency and safety. However, current wearable sensing devices are often uncomfortable and imprecise. Furthermore, stable methods for fatigue detection are not yet established. To address these challenges, this paper introduces 3D printing and deep learning to design a smart wearable sensing device to detect different states of sports fatigue. First, to meet the need for comfort and improved accuracy in data collection, we utilized reverse engineering and additive manufacturing technologies. Second, we designed a prototype based on the long short-term memory (LSTM) neural network to analyze the collected bioelectrical signals for the identification of sports fatigue states and the extraction of related indicators. Finally, we conducted a large number of numerical experiments. The results demonstrated that our prototype and related equipment could collect signals and mine information as well as identify indicators associated with sports fatigue in the signals, thereby improving accuracy in the classification of fatigue states. Full article

Presenting real-world paths in property graphs is a complex challenge of identifying and representing the properties of routes and their environments. These property graphs serve as foundational datasets for generating smart sports training routes, where route features such as terrain, bends, and hills critically influence the route design. This paper outlines a method for identifying key parameters of real-world paths and encoding them into property graphs. The proposed method has significant implications for sports event planning, particularly in designing route-based training that meets specific athletic challenges. The research concludes by presenting a case study in which a property graph that enables cycling route generation was created for the country of Slovenia, and a sample training route was generated. Full article

Temporal parameters are crucial for understanding running performance, especially in elite sports environments. Traditional measurement methods are often labor-intensive and not suitable for field conditions. This study seeks to provide greater clarity in parameter estimation using a single device by comparing it to the gold standard. Specifically, this study aims to investigate how the temporal parameters and vertical stiffness (K_vert) of running stride exerted by IMU sensors are related to the parameters of the smart insole for outdoor acquisition. Ten healthy male subjects performed four 60-meter high-speed runs. Data were collected using the WIMU PRO™ device and smart insoles. Contact time (CT) and flight time (FT) were identified, and K_vert was calculated using Morin’s method. Statistical analyses assessed data normality, correlations, and reliability. WIMU measured longer CT, with differences ranging from 26.3% to 38.5%, and shorter FT, with differences ranging from 27.3% to 54.5%, compared to smart insoles, across different running speeds. K_vert values were lower with WIMU, with differences ranging from 23.96% to 45.01% depending on the running activity, indicating significant differences (p < 0.001). Using these results, a multiple linear regression model was developed for the correction of WIMU’s K_vert values, improving the accuracy. The improved accuracy of K_vert measurements has significant implications for athletic performance. It provides sports scientists with a more reliable metric to estimate player fatigue, potentially leading to more effective training regimens and injury prevention strategies. This advancement is particularly valuable in team sports settings, where easy-to-use and accurate biomechanical assessments of multiple athletes are essential. Full article

Previous studies on gender differences in running biomechanics have predominantly been limited to joint angles and have not investigated a potential influence of footwear condition. This study shall contribute to closing this gap. Lower body biomechanics of 37 recreational runners (19 f, 18 m) were analysed for eight footwear and two running speed conditions. Presenting the effect size Cliff’s Delta enabled the interpretation of gender differences across a variety of variables and conditions. Known gender differences such as a larger range of hip movement in female runners were confirmed. Further previously undiscovered gender differences in running biomechanics were identified. In women, the knee extensors are less involved in joint work. Instead, compared to men, the supinators contribute more to deceleration and the hip abductors to acceleration. In addition to differences in extent, women also show a temporal delay within certain variables. For the foot, ankle and shank, as well as for the distribution of joint work, gender differences were found to be dependent on footwear condition, while sagittal pelvis and non-sagittal hip and thigh kinematics are rather consistent. On average, smaller gender differences were found for an individual compared to a uniform running speed. Future studies on gender differences should consider the influence of footwear and running speed and should provide an accurate description of the footwear condition used. The findings of this study could be used for the development of gender-specific running shoes and sports and medical products and provide a foundation for the application of smart wearable devices in gender-specific training and rehabilitation. Full article

Human Activity Recognition (HAR) is an essential area of research in Artificial Intelligence and Machine Learning, with numerous applications in healthcare, sports science, and smart environments. While several advancements in the field, such as attention-based models and Graph Neural Networks, have made great strides, this work focuses on data augmentation methods that tackle issues like data scarcity and task variability in HAR. In this work, we investigate and expand the use of mixup and cutout data augmentation methods to sensor-based and skeleton-based HAR datasets. These methods were first widely used in Computer Vision and Natural Language Processing. We use both augmentation techniques, customized for time-series and skeletal data, to improve the robustness and performance of HAR models by diversifying the data and overcoming the drawbacks of having limited training data. Specifically, we customize mixup data augmentation for sensor-based datasets and cutout data augmentation for skeleton-based datasets with the goal of improving model accuracy without adding more data. Our results show that using mixup and cutout techniques improves the accuracy and generalization of activity recognition models on both sensor-based and skeleton-based human activity datasets. This work showcases the potential of data augmentation techniques on transformers and Graph Neural Networks by offering a novel method for enhancing time series and skeletal HAR tasks. Full article

Yoga enhances acceptance, compassion, physicality, mental and emotional awareness, and spiritual benefits through breath techniques, postures, and body locks, while swimming improves flexibility, strength, and body awareness. The fusion of yoga and swimming, particularly the aqua yoga asana method, offers a balanced lifestyle for athletes and non-performers, enhancing their performance. Our study examined the feasibility of incorporating yoga and swimming practice into Romanian subjects’ lifestyles, designing a factor analysis in SmartPLS software, based on an online survey. This study assessed participants’ knowledge of yoga’s theory and philosophy, as well as their perceptions of the benefits of swimming practice for social and health issues. Our 250 young swimming athletes train in Bucharest’s sports clubs. According to our study, Romanian participants practice yoga and swimming as often as possible to reduce stress, improve concentration for work-related tasks, and improve joint elasticity, balance, and muscular tone. The high coefficient of path analysis (0.667) proved that those who practice yoga asanas have a high level of awareness and understand the fundamentals of the practice. The second coefficient of path analysis (0.857) shows that those who understand yoga better are convinced of its positive effects on society and their health. Thus, yoga and swimming are substitutes for other approaches in prevention and therapy, making it a beneficial tool for pre-performance swimming. Full article

In cross-country skiing, ski poles play a crucial role in technique, propulsion, and overall performance. The kinematic parameters of ski poles can provide valuable information about the skier’s technique, which is of great significance for coaches and athletes seeking to improve their skiing performance. In this work, a new smart ski pole is proposed, which combines the uniaxial load cell and the inertial measurement unit (IMU), aiming to provide comprehensive data measurement functions more easily and to play an auxiliary role in training. The ski pole can collect data directly related to skiing technical actions, such as the skier’s pole force, pole angle, inertia data, etc., and the system’s design, based on wireless transmission, makes the system more convenient to provide comprehensive data acquisition functions, in order to achieve a more simple and efficient use experience. In this experiment, the characteristic data obtained from the ski poles during the Double Poling of three skiers were extracted and the sample t-test was conducted. The results showed that the three skiers had significant differences in pole force, pole angle, and pole time. Spearman correlation analysis was used to analyze the sports data of the people with good performance, and the results showed that the pole force and speed (r = 0.71) and pole support angle (r = 0.76) were significantly correlated. In addition, this study adopted the commonly used inertial sensor data for action recognition, combined with the load cell data as the input of the ski technical action recognition algorithm, and the recognition accuracy of five kinds of cross-country skiing technical actions (Diagonal Stride (DS), Double Poling (DP), Kick Double Poling (KDP), Two-stroke Glide (G2) and Five-stroke Glide (G5)) reached 99.5%, and the accuracy was significantly improved compared with similar recognition systems. Therefore, the equipment is expected to be a valuable training tool for coaches and athletes, helping them to better understand and improve their ski maneuver technique. Full article

Recently, the research community has shown significant interest in the continuous temporal data obtained from motion sensors in wearable devices. These data are useful for classifying and analysing different human activities in many application areas such as healthcare, sports and surveillance. The literature has presented a multitude of deep learning models that aim to derive a suitable feature representation from temporal sensory input. However, the presence of a substantial quantity of annotated training data is crucial to adequately train the deep networks. Nevertheless, the data originating from the wearable devices are vast but ineffective due to a lack of labels which hinders our ability to train the models with optimal efficiency. This phenomenon leads to the model experiencing overfitting. The contribution of the proposed research is twofold: firstly, it involves a systematic evaluation of fifteen different augmentation strategies to solve the inadequacy problem of labeled data which plays a critical role in the classification tasks. Secondly, it introduces an automatic feature-learning technique proposing a Multi-Branch Hybrid Conv-LSTM network to classify human activities of daily living using multimodal data of different wearable smart devices. The objective of this study is to introduce an ensemble deep model that effectively captures intricate patterns and interdependencies within temporal data. The term “ensemble model” pertains to fusion of distinct deep models, with the objective of leveraging their own strengths and capabilities to develop a solution that is more robust and efficient. A comprehensive assessment of ensemble models is conducted using data-augmentation techniques on two prominent benchmark datasets: CogAge and UniMiB-SHAR. The proposed network employs a range of data-augmentation methods to improve the accuracy of atomic and composite activities. This results in a 5% increase in accuracy for composite activities and a 30% increase for atomic activities. Full article

This work presents a smart mask for real-time monitoring of carbon dioxide (CO₂) levels as a reference tool for diagnosis, sports training and mental health status. A printed circuit board was projected and fabricated to gain data with real-time visualization and storage on a database, enabling remote monitoring as a needed skill for telemedicine purposes. The electronics were inserted in a wearable device—shaped like a mask—and 3D-printed with biocompatible materials. The whole device was used for analyzing CO₂ on a breath volunteer in three kinds of measurement. Full article

E-textiles have emerged as a fast-growing area in wearable technology for sports and fitness due to the soft and comfortable nature of textile materials and the capability for smart functionality to be integrated into familiar sports clothing. This review paper presents the roles of wearable technologies in sport and fitness in monitoring movement and biosignals used to assess performance, reduce injury risk, and motivate training/exercise. The drivers of research in e-textiles are discussed after reviewing existing non-textile and textile-based commercial wearable products. Different sensing components/materials (e.g., inertial measurement units, electrodes for biosignals, piezoresistive sensors), manufacturing processes, and their applications in sports and fitness published in the literature were reviewed and discussed. Finally, the paper presents the current challenges of e-textiles to achieve practical applications at scale and future perspectives in e-textiles research and development. Full article

A wide range of applications, including sports and healthcare, use human activity recognition (HAR). The Internet of Things (IoT), using cloud systems, offers enormous resources but produces high delays and huge amounts of traffic. This study proposes a distributed intelligence and dynamic HAR architecture using smart IoT devices, edge devices, and cloud computing. These systems were used to train models, store results, and process real-time predictions. Wearable sensors and smartphones were deployed on the human body to detect activities from three positions; accelerometer and gyroscope parameters were utilized to recognize activities. A dynamic selection of models was used, depending on the availability of the data and the mobility of the users. The results showed that this system could handle different scenarios dynamically according to the available features; its prediction accuracy was 99.23% using the LightGBM algorithm during the training stage, when 18 features were used. The prediction time was around 6.4 milliseconds per prediction on the smart end device and 1.6 milliseconds on the Raspberry Pi edge, which can serve more than 30 end devices simultaneously and reduce the need for the cloud. The cloud was used for storing users’ profiles and can be used for real-time prediction in 391 milliseconds per request. Full article

Force-based human posture estimation (FPE) provides a valuable alternative when camera-based human motion capturing is impractical. It offers new opportunities for sensor integration in smart products for patient monitoring, ergonomic optimization and sports science. Due to the interdisciplinary research on the topic, an overview of existing methods and the required expertise for their utilization is lacking. This paper presents a systematic review by the PRISMA 2020 review process. In total, 82 studies are selected (59 machine learning (ML)-based and 23 digital human model (DHM)-based posture estimation methods). The ML-based methods use input data from hardware sensors—mostly pressure mapping sensors—and trained ML models for estimating human posture. The ML-based human posture estimation algorithms mostly reach an accuracy above 90%. DHMs, which represent the structure and kinematics of the human body, adjust posture to minimize physical stress. The required expert knowledge for the utilization of these methods and their resulting benefits are analyzed and discussed. DHM-based methods have shown their general applicability without the need for application-specific training but require expertise in human physiology. ML-based methods can be used with less domain-specific expertise, but an application-specific training of these models is necessary. Full article

Emerging smart devices have gathered increasing popularity within the sports community, presenting a promising avenue for enhancing athletic performance. Among these, the Rise Dynamics Alpha (RD $\alpha$) smart gloves exemplify a system designed to quantify boxing techniques. The objective of this study is to expand upon the existing RD $\alpha$ system by integrating machine-learning models for striking technique and target object classification, subsequently validating the outcomes through empirical analysis. For the implementation, a data-acquisition experiment is conducted based on which the most common supervised ML models are trained: decision tree, random forest, support vector machine, k-nearest neighbor, naive Bayes, perceptron, multi-layer perceptron, and logistic regression. Using model optimization and significance testing, the best-performing classifier, i.e., support vector classifier (SVC), is selected. For an independent evaluation, a final experiment is conducted with participants unknown to the developed models. The accuracy results of the data-acquisition group are 93.03% (striking technique) and 98.26% (target object) and for the independent evaluation group 89.55% (striking technique) and 75.97% (target object). Therefore, it is concluded that the system based on SVC is suitable for target object and technique classification. Full article