Evaluación de una raqueta con sensor disponible comercialmente como herramienta de diagnóstico y entrenamiento para bádminton de élite

To avoid the drawbacks of optical video-based motion capture systems and due to the ongoing miniaturization of integrated sensors, an increasing variety of sensor-based systems has been used for motion capture in sports. Meanwhile, there are ready-made, commercially available solutions that claim to be capable of recording reliable kinematic data. This research project focuses on the question of whether a commercially available badminton racket with an integrated sensor device (Oliver® Plasma TX 5) provides meaningful data for diagnostic and training purposes in elite sports. Therefore, 16 elite badminton players executed jump smashes using this sensor racket while the kinematics of the stroke technique were recorded using a high speed video-based system. Bland-Altman plots were applied to analyze the agreement between the two systems. The plots revealed a systematic bias and 95% limits of agreement ranging from 6% to 23%: The detection of stroke techniques showed a 42% rate of success. These data show that the measurement accuracy of the sensor racket is not sufficient for use in diagnostics or training. Future development of the sensor racket could include a method to calibrate the system prior to a measurement, in addition to correcting the underlying algorithm to reduce the bias.Para evitar las desventajas de los sistemas ópticos de captura de movimiento basados en video y debido a la continua miniaturización de los sensores integrados, una creciente variedad de sistemas basados en sensores se ha usado para la captura de movimiento en deportes. Entretanto, existen soluciones ya terminadas y comercialmente disponibles que afirman ser capaces de registrar datos cinemáticos confiables. Este proyecto de investigación se enfoca en la pregunta de si una raqueta de bádminton disponible comercialmente con un sensor integrado (Oliver® Plasma TX 5) proporciona datos relevantes para el diagnóstico y entrenamiento en deportes de élite. Por tanto, 16 jugadores de bádminton de élite ejecutaron remates en salto usando la raqueta con sensor mientras la cinemática de la técnica del golpe era grabada con un sistema de alta velocidad basado en video. Los gráficos de Bland-Altman se usaron para analizar la concordancia entre los dos sistemas. Los gráficos revelaron un sesgo sistemático y límites de concordancia del 95% entre 6% y 23%. La detección de las técnicas del golpe evidenció una tasa de éxito del 42%. Estos datos demuestran que la precisión en la medición de la raqueta con sensor no es suficiente para usarla en diagnóstico o entrenamiento. El desarrollo futuro de la raqueta con sensor podría incluir un método para calibrar el sistema antes de hacer una medición, además de corregir el algoritmo subyacente para reducir el sesgo

Evaluación de una raqueta con sensor disponible comercialmente como herramienta de diagnóstico y entrenamiento para bádminton de élite

To avoid the drawbacks of optical video-based motion capture systems and due to the ongoing miniaturization of integrated sensors, an increasing variety of sensor-based systems has been used for motion capture in sports. Meanwhile, there are ready-made, commercially available solutions that claim to be capable of recording reliable kinematic data. This research project focuses on the question of whether a commercially available badminton racket with an integrated sensor device (Oliver® Plasma TX 5) provides meaningful data for diagnostic and training purposes in elite sports. Therefore, 16 elite badminton players executed jump smashes using this sensor racket while the kinematics of the stroke technique were recorded using a high speed video-based system. Bland-Altman plots were applied to analyze the agreement between the two systems. The plots revealed a systematic bias and 95% limits of agreement ranging from 6% to 23%: The detection of stroke techniques showed a 42% rate of success. These data show that the measurement accuracy of the sensor racket is not sufficient for use in diagnostics or training. Future development of the sensor racket could include a method to calibrate the system prior to a measurement, in addition to correcting the underlying algorithm to reduce the bias.Para evitar las desventajas de los sistemas ópticos de captura de movimiento basados en video y debido a la continua miniaturización de los sensores integrados, una creciente variedad de sistemas basados en sensores se ha usado para la captura de movimiento en deportes. Entretanto, existen soluciones ya terminadas y comercialmente disponibles que afirman ser capaces de registrar datos cinemáticos confiables. Este proyecto de investigación se enfoca en la pregunta de si una raqueta de bádminton disponible comercialmente con un sensor integrado (Oliver® Plasma TX 5) proporciona datos relevantes para el diagnóstico y entrenamiento en deportes de élite. Por tanto, 16 jugadores de bádminton de élite ejecutaron remates en salto usando la raqueta con sensor mientras la cinemática de la técnica del golpe era grabada con un sistema de alta velocidad basado en video. Los gráficos de Bland-Altman se usaron para analizar la concordancia entre los dos sistemas. Los gráficos revelaron un sesgo sistemático y límites de concordancia del 95% entre 6% y 23%. La detección de las técnicas del golpe evidenció una tasa de éxito del 42%. Estos datos demuestran que la precisión en la medición de la raqueta con sensor no es suficiente para usarla en diagnóstico o entrenamiento. El desarrollo futuro de la raqueta con sensor podría incluir un método para calibrar el sistema antes de hacer una medición, además de corregir el algoritmo subyacente para reducir el sesgo

SPACE-TIME GRAPH-BASED CONVOLUTIONAL NEURAL NETWORKS OF STUDY ON MOVEMENT RECOGNITION OF FOOTBALL PLAYERS

Behaviour recognition technology is an interdisciplinary technology, integrating many research achievements in computer vision, deep learning, pattern recognition and other fields. The key information of bone data on human behavior can not only accurately describe the motion posture of the human body in three-dimensional space, but also its rigid connection structure is robust to various external interference factors. However, the behavioral recognition algorithm is influenced by different factors such as background, light and environment, which is easy to lead to unstable recognition accuracy and limited application scenarios. To address this problem, in this paper, we propose a noise filtering algorithm based on data correlation and skeleton energy model filtering, construct a set of football player data sets, using the ST-GCN algorithm to train the skeleton characteristics of football players, and construct a behavior recognition system applied to football players. Finally, by comparing the accuracy of Deep LSTM, 2s-AGCN and the algorithm in this paper, the accuracy of TOP1 and TOP5 is 39.97% and 66.34%, respectively, which are significantly higher than the other two algorithms. It can realize the statistics of athletes and analyze the technical and tactical movements of players on the football field

El uso de la tecnología de captura de movimiento para el análisis del rendimiento deportivo

In sport performance, motion capture aims at tracking and recording athletes’ human motion in real time to analyze physical condition, athletic performance, technical expertise and injury mechanism, prevention and rehabilitation. The aim of this paper is to systematically review the latest developments of motion capture systems for the analysis of sport performance. To that end, selected keywords were searched on studies published in the last four years in the electronic databases ISI Web of Knowledge, Scopus, PubMed and SPORTDiscus, which resulted in 892 potential records. After duplicate removal and screening of the remaining records, 81 journal papers were retained for inclusion in this review, distributed as 53 records for optical systems, 15 records for non-optical systems and 13 records for markerless systems. Resultant records were screened to distribute them according to the following analysis categories: biomechanical motion analysis, validation of new systems and performance enhancement. Although optical systems are regarded as golden standard with accurate results, the cost of equipment and time needed to capture and postprocess data have led researchers to test other technologies. First, non-optical systems rely on attaching sensors to body parts to send their spatial information to computer wirelessly by means of different technologies, such as electromagnetic and inertial (accelerometry). Finally, markerless systems are adequate for free, unobstructive motion analysis since no attachment is carried by athletes. However, more sensors and sophisticated signal processing must be used to increase the expected level of accuracy.En el ámbito del rendimiento deportivo, el objetivo de la captura de movimiento es seguir y registrar el movimiento humano de deportistas para analizar su condición física, rendimiento, técnica y el origen, prevención y rehabilitación de lesiones. En este artículo, se realiza una revisión sistemática de los últimos avances en sistemas de captura de movimiento para el análisis del rendimiento deportivo. Para ello, se buscaron palabras clave en estudios publicados en los últimos cuatro años en las bases de datos electrónicas ISI Web of Knowledge, Scopus, PubMed y SPORTDiscus, dando lugar a 892 registros. Tras borrar duplicados y análisis del resto, se seleccionaron 81 artículos de revista, distribuidos en 53 registros para sistemas ópticos, 15 para sistemas no ópticos y 13 para sistemas sin marcadores. Los registros se clasificaron según las categorías: análisis biomecánico, validación de nuevos sistemas y mejora del rendimiento. Aunque los sistemas ópticos son los sistemas de referencia por su precisión, el coste del equipamiento y el tiempo invertido en la captura y postprocesado ha llevado a los investigadores a probar otras tecnologías. En primer lugar, los sistemas no ópticos se basan en adherir sensores a zonas corporales para mandar su información espacial a un ordenador mediante distintas tecnologías, tales como electromagnética y inercial (acelerometría). Finalmente, los sistemas sin marcadores permiten un análisis del movimiento sin restricciones ya que los deportistas no llevan adherido ningún elemento. Sin embargo, se necesitan más sensores y un procesado de señal avanzado para aumentar el nivel de precisión necesario

Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning

Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality

Sensor-based assessment using machine learning for predictive model of badminton skills

Badminton assessment is a process to evaluate the performance of players and it is very important for them to identify their strengths and weaknesses so as to improve their training effectiveness. Several conventional assessment methods, which are the lack of manpower, expertise and objective methods. Besides, standard parameters and assessment model using machine learning for badminton assessment are still at research level. The main objective of this research is to design and develop a novel and effective system for badminton assessment . In this thesis, a total of three assessment modules (Module 1: Badminton Serving Accuracy, Module 2: Badminton Shots Quality, Module 3: Player’s Agility) were developed to extract the required measurable parameters of players through their serves, hits and agility. A 9 degree of freedom wireless sensor, an APDM Opal sensor and a badminton feedback sensor, XiaoYu 2.0 were used in this study to collect kinematic parameters such as acceleration , power and rotational speed. All the three modules were tested with 3 strong and 6 normal players and there were totally 46 collected features. A total of 39 out of 46 features have been proved being significantly different using t-test method. The three feature selection methods were named Relief, Principal Component Analysis and Correlation Feature Selection and were used for feature extraction. Then, the acquired datasets were tested by seven machine learning models , namely Random Tree (RT), Random Forest, Artificial Neural Network, K Star, Multiple Linear Regression, Gaussian Process and Support Vector Machine. Total of 21 assessment models had been constructed. The results show that the RT model produces prediction accuracy of 90.84% and correlation value of r=0.86

MARKERLESS MOTION CAPTURE WITHIN SPORT: AN EXPLORATORY CASE STUDY

The purpose of this case study was to compare centre of mass (CoM) recorded by a markerless motion capture system (60 Hz) to a criterion marker based system (120 Hz). Gait kinematics of one healthy male participant was recorded five times by both capture systems simultaneously. CoM position was assessed using a full body six degrees of freedom model, normalised to the stance phase based on a 20 N vertical force threshold recorded with force plates. T-tests on RMSE indicated frontal (0.002 m) and sagittal (0.066 m) CoM coordinates were not significantly different between systems, transverse CoM (0.020 m) was significantly different. Statistical parametric mapping showed significant difference in sagittal CoM during the last 20% of stance. Markerless systems show promise in accurately assessing CoM. Future work should focus on sport actions with larger cohorts