Synchronizing Sensed Data in Team Sports

55 When dealing with sensor data for a team sport, it is often useful to be able to query across multiple sensors and thus to be able to compare data from several players for any given moment in time. In order to do this, the data from all sensors must be synchronized so that the start time of the game or activity can be identified in the data from each individual sensor. This is necessary because sensor devices may be activated asynchronously, since the device begins recording when it first comes into contact with the player's skin. While many sensor devices will record a start time, this information is not necessarily reliable as there is often no correlation between the system time and the time kept by the match officials, or indeed between the times on any two sensors. In addition, the devices may be unreliable and may malfunction, or the device may become detached during the course of the game. From an abstract perspective, sensors can be regarded as generating values that correspond to various states, eg first half, second half etc. A 'profile' is a combination of various states. Each state occurs once and in the order specified. The goal is to semantically enrich sensor data with an additional field that identifies the state associated with every sensor reading. Our method is to convert the sensor stream to XML, which facilitates the subsequent semantic enrichment process. In simple terms, the synchronization process involves identifying one or more specific moments in time, such as the beginning or end of the game. Once the reading corresponding to that time is identified, the data can be synchronized with the data from all the other devices involved in the experiment. The sensors used in our experiments record a heart rate value every 5 seconds , and approximately 1200 values are generated while the device is worn. The six states corresponding to a Gaelic football match can be seen in Figure 1. This example graph is for a midfield player who has a profile of gradually increasing activity through Pre-Game and Warm-Up, and remaining constantly active throughout each half. This profile can be easily split into states because of the period of rest located between the two periods of high activity. However, this profile is atypical among the thirty players involved in a given game. A more typical graph is shown in Figure …