Common Sport Movement Recognition from Wearable Inertial Sensor
Norazman Shahar1, Nurul Fathiah Ghazali2, NurAkmal Yahya3, Muhammad Amir As’ari4
1Muhammad Amir as’ari*, Sport Innovation and Technology Center (SITC), School of Biosciences and Medical Engineering, Johor Bahru, Univeristi Teknologi Malaysia.
2Norazman Shahar, Sport Innovation and Technology Center (SITC), School of Biosciences and Medical Engineering, Johor Bahru, Univeristi Teknologi Malaysia.
3M Nurul Fathiah Ghazali, Sport Innovation and Technology Center (SITC), School of Biosciences and Medical Engineering, Johor Bahru, Univeristi Teknologi Malaysia.
4NurAkmal Yahya, Sport Innovation and Technology Center (SITC), School of Biosciences and Medical Engineering, Johor Bahru, Univeristi Teknologi Malaysia.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1285-1292 | Volume-8 Issue-5, January 2020. | Retrieval Number: E4597018520/2020©BEIESP | DOI: 10.35940/ijrte.E4597.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Common sport movements are the fundamental movements in all kind of sports. There are lots of researches done on classifying sports movements but very few are focused on common sport movement which is the focus of this project. The main aim is to develop an automated algorithm that can detect the common sport movements into walking based and jumping based movement from the wearable inertial sensor. The inertial sensor signals obtained from ten subjects were processed and grouped into walking-based and jumping-based movements. Time-domain features were extracted from the signals. Finally, the classification and performance evaluation process is done by using three different classification models (Support Vector Machine (SVM), k Nearest Neighbor (k-NN) and Decision Tree) with fixed window size of 1.28 seconds at the first stage. At the second stage, the best model from the first stage was used to determine the best window size in extracting the features that represent the walking and jumping based movement. As a result, SVM algorithm with window size of 2 seconds produced the highest overall accuracy of 95.4 % which proved to be the best classification algorithm to classify the common sport movements into walking-based and jumping-based movements. It is hoped that the outcome from this project can be used as a part of developing the overall automated sport movement recognition which is useful for the analyst, coach or player to analyse the performance of the player as well as predicting total energy consumption in preventing the injury among the player.
Keywords: Common Sport Movement Recognition, Decision Tree, k-Nearest Neighbors, Inertial Sensor, Support Vector Machine.
Scope of the Article: Machine Design.