Wearable microtechnology can accurately identify collision events during professional rugby league match-play

Abstract

Objectives: Collision frequency during rugby league matches is associated with team success, greater and longer lasting fatigue and increased injury risk. This study researched the sensitivity and specificity of microtechnology to count collision events during rugby league matches. Design: Diagnostic accuracy studyMethods: While wearing a microtechnology device (Catapult, Optimeye S5), eight professional rugby league players were subjected to a total of 380 collision events during matches. Video footage of each match was synchronised with microtechnology data. The occurrence of each collision event was coded in comparison with whether that event was or was not detected by microtechnology.Results: Microtechnology detected 371 true-positive collision events (sensitivity = 97.6 ± 1.5%). When low-intensity (<1 PlayerLoad AU), short duration (<1 second) events were excluded from the analysis, specificity was 91.7 ± 2.5%, accuracy was 92.7 ± 1.3%, positive likelihood ratio was 11.4 ×/÷ 1.4 and the typical error of estimate was 7.8% ×/÷ 1.9 (d = 0.29 ×/÷ 1.9, small). Microtechnology collisions 2 were strongly and positively correlated with video coded collision events (r = 0.96). The ability of microtechnology to detect collision events improved as the intensity and duration of the collision increased. Conclusions:Microtechnology can identify 97.6% of collision events during rugby league match-play.The typical error associated with measuring contact events can be reduced to 7.8%, with accuracy (92.7%) and specificity (91.7%) improving, when low-intensity (<1 PlayerLoad AU) and short duration (<1 second) collision reports are excluded. This provides practitioners with a measurement of contact workload during professional rugby league matches.

Development of a Human Activity Recognition System for Ballet Tasks

“…Convolutional neural networks have previously been applied to a single wearable sensor's accelerometer output to identify 10 different specific strikes in beach volleyball at a single level of classification with a lower classification accuracy of 83.2% [33]. The results of the current study are closer to those of machine learning programmes which have been developed for the recognition of bowling tasks in cricket (99% specificity and 98.1% sensitivity) [25] and tackles in rugby (97.6% accuracy) [27]. While manufacturer-developed algorithms have been developed to detect jumping on other sporting populations with similar accuracy, these have not been validated in dancespecific jumps [14,15].…”

“…Additionally, the aesthetics of ballet focus on clean, unimpeded movements and line of the leg and torso, in both training and performance settings [36]. It is unlikely that an elite dancer or athlete would regularly wear six sensors and within other sports a single upper back worn sensor is more common [25,27]. Our study demonstrated a single sensor worn on the upper back having the poorest accuracy.…”

“…1. Sensors were placed on the thoracic spine (used in previous sporting activity recognition research [25][26][27]), sacrum (recommended as this is close to an individual's centre of mass [18]) and lower limbs (to capture lower limb movement). On the lower limbs, sensors were placed bilaterally in order to detect the different asymmetrical movements of dance.…”

Quantification of Internal and External Load in School Football According to Gender and Teaching Methodology

“…The teacher and tactical [31,32,35], as well as neuromuscular (player load, impacts, etc.) [36,37] effort required to play football. Similarly, to quantify eTL subjectively, one can use the Integral System for the Analysis of Training Tasks (SIATE in its Spanish acronym); this system studies the pedagogical and organizational variables that define a task, and also makes it possible for teachers to quantify the subjective eTL of the tasks used to teach contact sports (eTL task = sum of the assigned value, from 1 to 5 points, of each of the six categories of ordinal load variables: degree of opposition, density of the task, percentage of simultaneous performers, competitive load, game space, and cognitive implication) [38].…”

Using Smart Sensors to Monitor Physical Activity and Technical–Tactical Actions in Junior Tennis Players

“…Smart sensors are fast becoming key tools in performance analysis for decreasing the time of direct observation with optimal validity [13,16]. The symbiosis between both instruments (performance analysis and smart devices), has focused its development in high-performance sport, mainly [11] because of the desire for performance improvement and control of the training load in many sports, e.g., soccer [17,18], football [19], basketball [20], rugby [21,22], and tennis [3,13,23]. Meanwhile, the study in recreational and educational contexts is still at an early stage of development, meaning that the present and future usefulness of both tools cannot be known [24,25].…”