Influence of wearable biometric sensors on performance indicators of volleyball players

Wearable sensors are now very common in sports, animation, and healthcare as well as in other fields. Wearable sensors allow sportsmen to monitor their performance, identify ailments, and provide important understanding of game dynamics. Particularly volleyball requires a variety of difficult motions, including digs and blocks, which are absolutely essential for the result of the game. The main goal of this work is to provide a wearable sensor-based technique for automating the detection and identification of volleyball-related events like digs and blocks. This seeks to replace the manual procedure whereby statisticians mentally note events during games. Data collecting for this work uses five Xsens MTw Awinda sensors. Two classification algorithms—K Nearest Neighbour (KNN) and Linear Discriminant Analysis (LDA)—are combined with two separate cross-valuation methods. We evaluate the KNN method using k values ranging from 1 to 10. With both cross-valuation techniques validating this conclusion, LDA beats KNN in terms of average accuracy. LDA gets an average accuracy of 99.56 % and 89.56 % correspondingly when contrasting classifications with four and 10 classes. With KNN (k = 5), for four and ten classes respectively the average accuracies are 66.08 % and 92.39 %. This study shows how wearable sensors may be used to automatically detect and identify events connected to volleyball. The findings underline how better LDA is than KNN in reaching better average accuracy. These results can help to create more exact and effective techniques for monitoring and evaluating volleyball games.