If your hobbies aren’t generating data these days, do you even have any?
Sports take center stage in many other aspects of modern life, except maybe when it comes to the Internet of Things — barring the battle over privacy and wearables in the NBA
However, new research suggests that sports may soon see more data-collecting tech through a sport we only think while in a rec room or while watching Olympics. Table tennis. The introduction of self-powered, smart game tables that can help players improve their game through real-time data analysis.
The study, which was published this week in the journal Nature Communications, focuses specifically on ping-pong and on incorporating triboelectric sensors into a modified wood-based table surface in order to collect data on the ball’s velocity and position during a game. The team was able to create a ping-pong table by layering triboelectric sensors beneath a wood surface that had been chemically treated.
Because triboelectric sensors traditionally work by creating electrical charge after being separated from another material (similar to how charge is created when you detach a sock clinging to a freshly washed sweater), the impact and subsequent release of pressure experienced by these two surfaces when struck by a ping-pong ball was enough to create an electrical charge and the relevant velocity and position data.
While some real-time sports data collection methods do already exist, the authors of the new study say that such methods are primarily distributed, battery-powered sensors that suffer from high replacement and recharging costs as well as few environmental options for disposal. Comparatively, their self-powered alternative would not only be more sustainable, but thanks to its denser array of sensors, it could help players and referees better learn from gameplay as well.
“By analyzing the statistical result, the exercise habit data of athletes can be obtained for further guiding their training and developing better competition strategies,” write the authors.
When designing their smart ping-pong table, the authors also set their sights on solving a long-standing point of contention in ping-pong circles: when to call a top edge ball and when to call a side edge ball. The physical difference between hitting these two points of the table is small, but making the right call can be the difference between winning or losing a point.
To solve this problem the researchers also equipped the table with sensors at these points that when struck would either relay a definitive “edge of the surface” or “side of the table” call to a nearby computer. The authors write that the demonstrated accuracy in their study of this approach could help referees make quick, accurate judgments of these crucial calls during gameplay.
While this technology might not be useful for everyone’s daily lives, the study’s authors say it presents a “great opportunity” for big data analytics from such sports as well as opens a new door into the potential of wood-based electronics in combination with self-powered systems.
In the new era of internet of things, big data collection and analysis based on widely distributed intelligent sensing technology is particularly important. Here, we report a flexible and durable wood-based triboelectric nanogenerator for self-powered sensing in athletic big data analytics. Based on a simple and effective strategy, natural wood can be converted into a high-performance triboelectric material with excellent mechanical properties, such as 7.5-fold enhancement in strength, superior flexibility, wear resistance and processability. The electrical output performance is also enhanced by more than 70% compared with natural wood. A self-powered falling point distribution statistical system and an edge ball judgement system are further developed to provide training guidance and real-time competition assistance for both athletes and referees. This work can not only expand the application area of the self powered system to smart sport monitoring and assisting, but also promote the development of big data analytics in intelligent sports industry.