Activity Recognition of a Badminton Game Through Accelerometer and Gyroscope

The scope for doing physical exercises in daily life is declining day by day. But, the importance of human physical exercise for a healthy life, remains the same. It is necessary to generate a solution to simulate the outdoor experience of physical exercises and sports inside our home. In this paper, we propose an idea of recognizing the activities of a badminton game which has the potential to be useful in simulating the Badminton Sport. We have used motion sensors (e.g. Accelerometer, Gyroscope) to recognize different activities like, serve, smash, backhand, forehand, return etc. We have collected data from a large set of users and labeled their data over several instances. We have applied the K-Nearest Neighbors (k-NN) and Support Vector Machines (SVM) classifiers to recognize those activities. Existing approaches (e.g. Microsoft Xbox 360) used vision based techniques to recognize activities and use it in simulated games but we are using sensor based approach. Vision based approaches have some limitations such as slow rate of data, illumination constraints, occluded backgrounds etc. Our approach gives a low cost solution with a classification technique which is faster. The experimental result shows a decent recognition rate.