Common sport activity recognition using inertial sensor

• Published in: 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA) pp. 67 - 71
• Main Authors: Ghazali, N. F., Shahar, N., Rahmad, N. A., Sufri, N. A. J, As'ari, M. A., Latif, H. F. M
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2018
• Subjects: Accelerometers; Activity recognition; classifier; common sport recognition; Feature extraction; inertial sensor; Legged locomotion; Mathematical model; specific sport recognition; Support vector machines
• DOI: 10.1109/CSPA.2018.8368687

This paper aims to identify common sport activity recognition by using inertial sensor. Various classifiers from the Classification Learner App in MATLAB is used to recognize the common sport activity. In data collection, 10 subjects are asked to perform several common sport activities which is stationary, walking, jogging, sprinting, and jumping. The data is collected by using Physilog® 4 Silver which is an inertial sensor from Gait Up that is attached to the chest of the subject using strap. 3D accelerometer and gyroscope data from inertial sensor is observed and processed in this study. To recognize the common sport activity from the data, time-domain features such as mean, standard deviation, maximum and minimum value is extracted from the signal produced by inertial sensor. After that, the data is trained using the selected features and 10-fold cross validation is applied to set the training and testing data. The confusion matrix and accuracy result from each classifier is observed. In conclusion, Support Vector Machine with Cubic SVM is selected as the best model in recognizing common sport activity as it produced the highest accuracy, 91.2% in the confusion matrix. This classifier performance outperforms other classifiers in detecting five types of common sport activity from the data collected.