Badminton stroke identification using wireless inertial sensor and neural network

Abstract

Objective evaluation is essential in sports to monitor athlete performance, provide relevant and timely feedback, and minimize the risk of injury. Activity recognition is the first step in sport skill and technique performance analysis. This study investigated the use of wearable inertial sensors and a neural network (NN) to identify badminton strokes. The study also explored the effect of different NN configurations and a different number of sensors on the classification. Sensors were placed at the dominant wrist, left ankle, and right ankle. Six different strokes, ranging from soft hitting net shots to smashes, were performed with a total of 3300 repetitions from six well-trained badminton players. An automated window segmentation method was developed to identify the stroke instances. A scaled conjugate gradient training algorithm with two hidden layers and 55 neurons in each layer was found to be the best approach to classify badminton strokes with an accuracy of 97.69%. Even just wearing the inertial sensor on the wrist was sufficient, providing an accuracy of 95.09%. These results demonstrate the viability of using inertial sensors and NN to recognize badminton strokes, which can be applied in training and competitive environments.