Development and validation of a sensor-based algorithm for detecting visual exploratory actions

Wearable sensors are becoming widely used in the sport sciences to assess the performance of athletes. Advances in microelectromechanical systems technology, in particular inertial measurement units (IMUs), provide researchers and practitioners with a portable means of capturing performance in representative task scenarios. Of recent interest to sport scientists in team sports is how athletes perceive their surroundings and how visual (and other) information is used to select appropriate actions during a match. Collectively, the movements athletes make to gather information from their environments is referred to as exploratory action. An important aspect of this behavior is typically measured by notating (counting) the number of head turns from a third-person video perspective. A notational analysis is a labor-intensive task and prone to human error, especially when activity takes place over long durations. The IMUs are well suited to resolve these issues; they are highly accurate, very efficient, and have an adequate output data rate. Currently, no gold standard method exists to automatically detect head turn events from the IMUs. In the current study, a novel algorithm that utilizes data captured from a head-mounted IMU to count the number of head turns performed by an athlete during a controlled experimental task is presented. Results demonstrate that the presented algorithm is an appropriate and efficient method for assessing the number of head turns as a measure of exploratory actions.