A Focused Review on the Flexible Wearable Sensors for Sports: From Kinematics to Physiologies

Optimizing young tennis players' development: Exploring the impact of emerging technologies on training effectiveness and technical skills acquisition

The research analyzed the effect of weekly training plans, physical training frequency, AI-powered coaching systems, virtual reality (VR) training environments, wearable sensors on developing technical tennis skills, with and personalized learning as a mediator. It adopted a quantitative survey method, using primary data from 374 young tennis players. The model fitness was evaluated using confirmatory factor analysis (CFA), while the hypotheses were evaluated using structural equation modeling (SEM). The model fitness was confirmed through CFA, demonstrating high fit indices: CFI = 0.924, TLI = 0.913, IFI = 0.924, RMSEA = 0.057, and SRMR = 0.041, indicating a robust model fit. Hypotheses testing revealed that physical training frequency (β = 0.198, p = 0.000), AI-powered coaching systems (β = 0.349, p = 0.000), virtual reality training environments (β = 0.476, p = 0.000), and wearable sensors (β = 0.171, p = 0.000) significantly influenced technical skills acquisition. In contrast, the weekly training plan (β = 0.024, p = 0.834) and personalized learning (β = -0.045, p = 0.81) did not have a significant effect. Mediation analysis revealed that personalized learning was not a significant mediator between training methods/technologies and acquiring technical abilities. The results revealed that physical training frequency, AI-powered coaching systems, virtual reality training environments, and wearable sensors significantly influenced technical skills acquisition. However, personalized learning did not have a significant mediation effect. The study recommended that young tennis players' organizations and stakeholders consider investing in emerging technologies and training methods. Effective training should be given to coaches on effectively integrating emerging technologies into coaching regimens and practices.

Kinematic characteristics of elbow joint range of motion in elite Chinese freestyle swimmers with elbow pain during dry-land simulations of swimming strokes

The aim of this study was to obtain quantitative data on elbow joint ROM in elite freestyle swimmers with EP in China. Of the 50 elite freestyle swimmers recruited, 41 completed all measurements during dry-land swimming stroke simulations. Elbow joint angle, velocity, and acceleration were measured using inertial measurement units. The RMSE/D was calculated to determine the elbow joint ROM deviation. Joint angle (3.33  ∘ -42.96  ∘ ), angular velocity (-364.15 to 245.69  ∘ / s ), and angular acceleration (-7051.80 to 1465.35  ∘ / s 2 ) were significantly different between the critical pain and healthy. The probability distributions of joint angle (15.47  ∘ ±14.54  ∘ ), angular velocity (2.41  ∘ ±111.06  ∘ / s ), and angular acceleration (1.93 ± 2222.6  ∘ / s 2 ) in the slight pain group were significantly different betweenhealthy and critical pain. The RMSE/D distributions of angular velocity (28.3%) and acceleration (21.48%) in the critical pain deviated from the healthy. The peak value-RMSE/D matrix model obtained proved that elbow ROM significantly differed between the elite freestyle swimmers with EP and the healthy. Angular velocity and acceleration indicate the weakness and negative influence of kinematics on patients with EP. Thus, Potential solutions are to constantly optimise freestyle swimming techniques and strengthen the arm muscles.

Intelligent health and sport: An interplay between flexible sensors and basketball

Basketball, as one of the most popular sports in the world, has millions of followers and massive economic value. Basketball evolves so fast that it requires teams with smarter strategies, better skills, and stronger players. However, the competition strategies and training methods in basketball are still experience-based, lacking precise data to drive for more efficient training and strategies. On the other hand, flexible sensors, as a new class of sensors, have been a hotspot for scientific research and widely applied in various fields. Due to their excellent characteristics of flexibility, wearing comfort, convenience, and response speed, integrating flexible sensors into basketball has the potential to greatly promote all aspects of the sport. This paper aims to bring more fusion between basketball and flexible sensors. In this perspective, we first perform a review of the history of sensing technologies in the basketball sport and discuss mechanisms of flexible sensors applied on basketball players. Then specific scenarios for flexible sensors applied in basketball were elaborated on in detail. Finally, we envision the potential applications of flexible sensors in basketball and present our views on future development directions. We hope this paper can depict how flexible sensing technology is integrated into basketball systems and point out the future development of basketball with the help of flexible sensors.

Design and Characterization of Piezoresistive Sensors for Non-Planar Surfaces and Pressure Mapping: A Case Study on Kayak Paddle

This article focuses on the design of a sensor system for a non-planar surface, in particular a cylindrical shape, such as a kayak paddle. The main objective is to develop a piezoresistive sensor system to measure the pressure exerted by the hand on the shaft. The study begins with static characterization of the sensors, including dispersion analysis to assess their sensitivity, linearity and measurement range. A calibration process is carried out using a dedicated test bench, and an inverse viscoelastic model is used to establish an accurate relationship between the measured resistance and the corresponding pressure. The sensor system is connected to a data acquisition board equipped with an analog-to-digital converter (ADC) that enables the direct conversion of analog data into digital resistance values. Furthermore, Bluetooth Low Energy (BLE) wireless communication is employed to facilitate data transfer to a computer, enabling a detailed pressure mapping of the kayak paddle and real-time data collection. The calibrated sensors are then tested and validated on the kayak paddle, facilitating the mapping of pressure zones on the paddle surface. This mapping provides information for locating areas of high pressure exertion during kayaker movements.

Microfluidic Wearable Devices for Sports Applications

This study aimed to systematically review the application and research progress of flexible microfluidic wearable devices in the field of sports. The research team thoroughly investigated the use of life signal-monitoring technology for flexible wearable devices in the domain of sports. In addition, the classification of applications, the current status, and the developmental trends of similar products and equipment were evaluated. Scholars expect the provision of valuable references and guidance for related research and the development of the sports industry. The use of microfluidic detection for collecting biomarkers can mitigate the impact of sweat on movements that are common in sports and can also address the issue of discomfort after prolonged use. Flexible wearable gadgets are normally utilized to monitor athletic performance, rehabilitation, and training. Nevertheless, the research and development of such devices is limited, mostly catering to professional athletes. Devices for those who are inexperienced in sports and disabled populations are lacking. Conclusions: Upgrading microfluidic chip technology can lead to accurate and safe sports monitoring. Moreover, the development of multi-functional and multi-site devices can provide technical support to athletes during their training and competitions while also fostering technological innovation in the field of sports science.

Multidirectional Piezoelectric Vibration Energy Harvester Based on Cam Rotor Mechanism

The techniques that harvest mechanical energy from low-frequency, multidirectional environmental vibrations have been considered a promising strategy to implement a sustainable power source for wireless sensor networks and the Internet of Things. However, the obvious inconsistency in the output voltage and operating frequency among different directions may bring a hindrance to energy management. To address this issue, this paper reports a cam-rotor-based approach for a multidirectional piezoelectric vibration energy harvester. The cam rotor can transform vertical excitation into a reciprocating circular motion, producing a dynamic centrifugal acceleration to excite the piezoelectric beam. The same beam group is utilized when harvesting vertical and horizontal vibrations. Therefore, the proposed harvester reveals similar characterization in its resonant frequency and output voltage at different working directions. The structure design and modeling, device prototyping and experimental validation are conducted. The results show that the proposed harvester can produce a peak voltage of up to 42.4 V under a 0.2 g acceleration with a favorable power of 0.52 mW, and the resonant frequency for each operating direction is stable at around 3.7 Hz. Practical applications in lighting up LEDs and powering a WSN system demonstrate the promising potential of the proposed approach in capturing energy from ambient vibrations to construct self-powered engineering systems for structural health monitoring, environmental measuring, etc.

Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer

BACKGROUND

One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers.

METHODS

This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances.

RESULTS

The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97-99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%.

CONCLUSIONS

The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently.

Wearables and Machine Learning for Improving Runners' Motivation from an Affective Perspective

Wearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletes' performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In this paper, we present a wearable and a set of machine learning models that are able to deduce runners' emotions during their training. The solution is based on the analysis of runners' electrodermal activity, a physiological parameter widely used in the field of emotion recognition. As part of the DJ-Running project, we have used these emotions to increase runners' motivation through music. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training.