When faced with a fly ball approaching along the sagittal plane, fielders need information for the control of their running to the interception location. This information could be available in the initial part of the ball trajectory, such that the interception location can be predicted from its initial conditions. Alternatively, such predictive information is not available, and running to the interception location involves continuous visual guidance. The latter type of control would predict that fielders keep looking at the approaching ball for most of its flight, whereas the former type of control would fit with looking at the ball during the early part of the ball's flight; keeping the eyes on the ball during the remainder of its trajectory would not be necessary when the interception location can be inferred from the first part of the ball trajectory. The present contribution studied visual tracking of approaching fly balls. Participants were equipped with a mobile eye tracker. They were confronted with tennis balls approaching from about 20 m, and projected in such a way that some balls were catchable and others were not. In all situations, participants almost exclusively tracked the ball with their gaze until just before the catch or until they indicated that a ball was uncatchable. This continuous tracking of the ball, even when running close to their maximum speeds, suggests that participants employed continuous visual control rather than running to an interception location known from looking at the early part of the ball flight.
Access to performance data during matches and training sessions is important for coaches and players. Although there are many video tagging systems available which can provide such access, these systems require manual effort. Data from Inertial Measurement Units (IMU) could be used for automatically tagging video recordings in terms of players’ actions. However, the data gathered during volleyball sessions are generally very imbalanced, since for an individual player most time intervals can be classified as “non-actions” rather than “actions”. This makes automatic annotation of video recordings of volleyball matches a challenging machine-learning problem. To address this problem, we evaluated balanced and imbalanced learning methods with our newly proposed ‘super-bagging’ method for volleyball action modelling. All methods are evaluated using six classifiers and four sensors (i.e., accelerometer, magnetometer, gyroscope and barometer). We demonstrate that imbalanced learning provides better unweighted average recall, (UAR = 83.99%) for the non-dominant hand using a naive Bayes classifier than balanced learning, while balanced learning provides better performance (UAR = 84.18%) for the dominant hand using a tree bagger classifier than imbalanced learning. Our super-bagging method provides the best UAR (84.19%). It is also noted that the super-bagging method provides better averaged UAR than balanced and imbalanced methods in 8 out of 10 cases, hence demonstrating the potential of the super-bagging method for IMU’s sensor data. One of the potential applications of these novel models is fatigue and stamina estimation e.g., by keeping track of how many actions a player is performing and when these are being performed.
Before medieval dike building, the coastal area of the northern Netherlands was a wide, regularly inundated salt-marsh area. Despite the dynamic natural conditions, the area was inhabited already in the Iron Age. The inhabitants adapted to this marine environment by living on artificial dwelling mounds, so-called terps. Terp habitation was a highly successful way of life for over 1500 years, and may be re-introduced as a useful strategy for present and future communities in low-lying coastal regions that are facing accelerated sea-level rise. This already has been recommended in several reports, but detailed knowledge of the technology of terp habitation is usually lacking. The aim of this paper is to present nearly two decades of archaeological research in the coastal region of the northern Netherlands, in order to inform the current debate on the possibilities of adapting to the effects of climate change in low-lying coastal areas. It presents the multi-disciplinary methods of
How do outfielders control their locomotor behavior in running to catch fly balls? This question has been the topic of many empirical studies. It is interesting that a little addressed but highly relevant issue in this regard is that of the influence of perceived catchability on locomotor control. We examined what factors determine catchability and whether catchability can be reliably perceived. We had participants run to catch fly balls that could either be catchable or uncatchable. Participants performed two tasks. In the catching task, they were instructed to attempt to catch the ball and to keep running even when they felt that a ball was uncatchable. In the judging task, they were instructed to call "no" as soon as they perceived a ball to be uncatchable. Using Generalized Linear Mixed Effects Regression (GLMER) on data from the catching task, we modeled catchability, identifying five behaviorally relevant agent-environment variables that together explained 84.4% of the variance in catching performance. Next, we examined whether judgments of catchability were accurate. Using the GLMER-model, the catchability of every fly ball in the judging task was predicted and subsequently compared with participants' judgments. Participants were able to correctly judge the catchability of a fly ball on 85.4% of the trials. It is interesting that participants' judgments of fly balls to be uncatchable most often were given only after they had started running. Present findings provide a valuable step toward the formalization of an affordance-based control strategy for running to catch fly balls. (PsycINFO Database Record
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