Abstract:
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 di erent 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 Root-Mean-Square(RMS),K-Nearest Neighbors
(k-NN) and Support Vector Machines (SVM) and Dynamic Time Warping(DTW) classi ers
and 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 the slow rate of data,
illumination constraints, occluded backgrounds etc. Our approach gives a low cost solution with
a classi cation technique which is faster. The experimental result shows a decent recognition
rate.
