Detection of throwing in cricket using wearable sensors

One of the great controversies of the modern game of cricket is the determination of whether a bowler is using an illegal throw-like bowling action. Changes to the rules of cricket have reduced some of the confusion, yet, because of the complexities of the biomechanics of the arm it is difficult for an umpire to make a judgement on this issue. Expensive laboratory based testing has been able to quantify the action of a bowler and this testing is routinely used by cricket authorities to assess a bowling action. Detractors of the method suggest that it is unable to replicate match conditions, has long lead times for assessment and is only available to the elite. After extensive laboratory validation we present a technology and method for an in- game assessment using a wearable arm sensor for differentiating between a legal bowling action and throwing. The method uses inertial sensors on the upper and lower arm that do not impede the bowling action. Suspect deliveries, as assessed by an expert biomechanist using high speed video and motion capture reveal valid distinctive inertial signatures. The technology is an important step in the monitoring of bowling action on-field in near real-time. The technology is suitable for use in competition as well as a training tool for developing athletes.Griffith Sciences, Griffith School of EngineeringFull Tex

Validating an inertial measurement unit for cricket fast bowling: a first step in assessing the feasibility of diagnosing back injury risk in cricket fast bowlers during a tele-sport-and-exercise medicine consultation

This study aimed to validate an array-based inertial measurement unit to measure cricket fast bowling kinematics as a first step in assessing feasibility for tele-sport-and-exercise medicine. We concurrently captured shoulder girdle relative to the pelvis, trunk lateral flexion, and knee flexion angles at front foot contact of eight cricket medium-fast bowlers using inertial measurement unit and optical motion capture. We used one sample t-tests and 95% limits of agreement (LOA) to determine the mean difference between the two systems and Smallest Worth-while Change statistic to determine whether any differences were meaningful. A statistically significant (p < 0.001) but small mean difference of −4.7° ± 8.6° (95% Confidence Interval (CI) [−3.1° to −6.4°], LOA [−22.2 to 12.7], SWC 3.9°) in shoulder girdle relative to the pelvis angle was found between the systems. There were no statistically significant differences between the two systems in trunk lateral flexion and knee flexion with the mean differences being 0.1° ± 10.8° (95% CI [−1.9° to 2.2°], LOA [−22.5 to 22.7], SWC 1.2°) and 1.6° ± 10.1° (95% CI [−0.2° to 3.3°], LOA [−19.2 to 22.3], SWC 1.9°) respectively. The inertial measurement unit-based system tested allows for accurate measurement of specific cricket fast bowling kinematics and could be used in determining injury risk in the context of tele-sport-and-exercise-medicine

Full-Body Kinematics and Vertical Ground Reaction Forces in Elite Ten-Pin Bowling:A Field Study

The purpose was to investigate full-body kinematics and vertical ground reaction forces in the lower extremities of the delivery and to determine delivery changes over time after many deliveries in ten-pin bowling. Six male elite ten-pin bowlers completed six bouts of twelve bowling deliveries, all strike attempts, while measuring full-body kinematics and vertical ground reaction forces. Full-body joint angles, peak vertical ground reaction forces in the feet, vertical breaking impulse, centre of mass velocity, bowling score, and ball release velocity (BR vel) were measured. Results revealed that the BR vel was significantly decreased over bouts (p &lt; 0.001). Additionally, increased flexion of the dominant wrist (p &lt; 0.001) and elbow (p = 0.004) prior to ball release (BR) and increased pronation of the dominant wrist during BR (p = 0.034) were observed at later bouts. It was concluded that these kinematic changes in the dominant wrist and elbow prior to and during BR were performed to compensate for the change in traction between ball and lane during a bowling match. This, in turn, caused a decrease in BR vel. A conservation of energy perspective was discussed to highlight training applications and possibilities to enhance elite athletes’ bowling performance.</p

The Measurement of Sporting Performance using Mobile Physiological Monitoring Technology

Coaches are constantly seeking more ecologically valid and reliable data to improve professional sporting performance. Using unobtrusive, valid and reliable mobile physiological monitoring devices may assist in achieving this aim. For example, there is limited information regarding professional fast bowlers in cricket and understanding this role during competitive in-match scenarios rather than in simulated bowling events could enhance coaching and physical conditioning practices. The BioharnessTM is a mobile monitoring device and assesses 5 variables (Heart rate [HR], Breathing frequency [BF], Accelerometry [ACC], Skin temperature [ST] and Posture [P]) simultaneously. Therefore, the aims of this research were to assess the effectiveness of the BioharnessTM mobile monitoring device during professional sporting performance using fast bowlers in cricket and this was to be achieved in five research studies. Study 1 presented the physiological profile of professional cricketers reporting fitness data with other comparable professional athletes, with a specific interest in fast bowlers who were to be the focus of this work. The 2nd and 3rd study assessed the reliability and validity of the BioharnessTM through controlled laboratory based assessment. For validity, strong relationships (r = .89 to .99, P .89, P 79.2 beat.min-1) and BF (> 54.7 br.min-1). ACC presented excellent precision (r = .94, P .97, P 10 km.h-1) variables became more erroneous. HR and ACC were deemed as valid and reliable to be assessed during in-match sporting performance in study 5. This final study sought to utilise and assess the BioharnessTM device within professional cricket, assessing physiological responses of fast-medium bowlers within a competitive sporting environment, collected over three summer seasons. The BioharnessTM presented different physiological profiles for One Day (OD) and Multi Day (MD) cricket with higher mean HR (142 vs 137 beats.min-1, P < .05) and ACC (Peak acceleration (PkA) 227.6 vs 214.9 ct.episode-1, P < .01) values in the shorter match format. Differences in data for the varying match states of bowling (HR, 142 vs 137 beats.min-1, PkA 234.1 vs 226.6 ct.episode-1), between over (HR, 129 vs 120beats.min-1, PkA 136.4 vs 126.5 ct.episode-1) and fielding (115 vs 106 beats.min-1, PkA 1349.9 vs 356.1 ct.episode-1) were reported across OD and MD cricket. Therefore, this information suggests to the coach that the training regimes for fast bowlers should be specific for the different demands specific to the format of the game employed. Relationships between in-match BioharnessTM data and bowling performance were not clearly established due to the complexities of uncontrollable variables within competitive cricket. In conclusion, the BioharnessTM has demonstrated acceptable validity and reliability in the laboratory and the field setting for all variables (Heart rate, Breathing frequency, Accelerometry, Skin temperature and Posture) but with limitations for heart rate and breathing frequency at the more extreme levels of performance. Furthermore, taking these limitations into account it has successfully been utilised to assess performance and provide further insight into the physiological demands in the professional sport setting. Therefore, this work suggests that coaches and exercise scientists working together should seek to utilise new mobile monitoring technology to access unique insights in to sporting performance which may be unobtainable in the laboratory or a simulated field based event

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

Accelerometer validity to measure and classify movement in team sports

&nbsp;In team sports accelerometers are used to monitor the physical demands of athletic performance. Daniel\u27s research showed that accelerometer accuracy can be improved through filtering. He also showed that the accelerometer can be used to automatically classify the type of movement performed. Further improving the understanding of team sports

Vision Based Activity Recognition Using Machine Learning and Deep Learning Architecture

Human Activity recognition, with wide application in fields like video surveillance, sports, human interaction, elderly care has shown great influence in upbringing the standard of life of people. With the constant development of new architecture, models, and an increase in the computational capability of the system, the adoption of machine learning and deep learning for activity recognition has shown great improvement with high performance in recent years. My research goal in this thesis is to design and compare machine learning and deep learning models for activity recognition through videos collected from different media in the field of sports. Human activity recognition (HAR) mostly is to recognize the action performed by a human through the data collected from different sources automatically. Based on the literature review, most data collected for analysis is based on time series data collected through different sensors and video-based data collected through the camera. So firstly, our research analyzes and compare different machine learning and deep learning architecture with sensor-based data collected from an accelerometer of a smartphone place at different position of the human body. Without any hand-crafted feature extraction methods, we found that deep learning architecture outperforms most of the machine learning architecture and the use of multiple sensors has higher accuracy than a dataset collected from a single sensor. Secondly, as collecting data from sensors in real-time is not feasible in all the fields such as sports, we study the activity recognition by using the video dataset. For this, we used two state-of-the-art deep learning architectures previously trained on the big, annotated dataset using transfer learning methods for activity recognition in three different sports-related publicly available datasets. Extending the study to the different activities performed on a single sport, and to avoid the current trend of using special cameras and expensive set up around the court for data collection, we developed our video dataset using sports coverage of basketball games broadcasted through broadcasting media. The detailed analysis and experiments based on different criteria such as range of shots taken, scoring activities is presented for 8 different activities using state-of-art deep learning architecture for video classification