| Popis: |
Monitoring athletes with wearable sensors allows to gain insights on their technique and physical condition. However, invasive setups containing a large number of sensors may hinder the mobility of the athletes, leading to underperformance and possibly inaccurate data collection. In this paper, we show that correlation between data collected by different wearables can be used to identify a minimal setup. We propose a methodology to remove the least important sensors, and apply it to data collected by monitoring field hockey players. In this study, the number of sensors was reduced from 23 to 8 by deleting those exhibiting a correlation above 98%. Additionally, we demonstrate that even with a minimal sensor configuration, a significant amount of information is retained with regards to predicting the ball speed following a drag-flick, an important technique in field hockey. Our experiments indicate that the utility of the data for this specific task remains practically unaltered. |
