Investigation on the Effect of Different Window Size in Segmentation for Common Sport Activity

Signal segmentation is one of the most important processes in the activity recognition process. So far, windowing approaches is one of the commonly used segmentation technique to segment the data. The window size used to segment the data usually chosen based on the previous study and the effect of t...

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Published in2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) pp. 1 - 7
Main Authors Ghazali, Nurul Fathiah, As'ari, Muhammad Amir, Shahar, Norazman, Latip, Hadafi Fitri Mohd
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2018
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Abstract Signal segmentation is one of the most important processes in the activity recognition process. So far, windowing approaches is one of the commonly used segmentation technique to segment the data. The window size used to segment the data usually chosen based on the previous study and the effect of the activity recognition performance with the changes of window size is still vague and uncertain. Thus, in this study, we investigate the effect of different window size in segmentation process for common sports activity recognition. The study was conducted on ten subjects who wore a sensor from Gait Up called as Physilogic OR 4 Silver inertial sensor on their chest while performing several common sports activities such as stationary, walking, jogging, sprinting, and jumping. Three common used classifiers which are Decision Trees, k-Neighbor Nearest and Support Vector Machine were evaluated. Among the different ranges of window sizes tested, it was found that 2.5 seconds window size represents the best trade-off in recognition of common sports activity, with an obtained accuracy above 90%. From the result, it indicates that the selection of window size in segmentation process can affect the accuracy in detecting the common sports activity. The preferably employed window size in detecting the common sports activity is determined.
AbstractList Signal segmentation is one of the most important processes in the activity recognition process. So far, windowing approaches is one of the commonly used segmentation technique to segment the data. The window size used to segment the data usually chosen based on the previous study and the effect of the activity recognition performance with the changes of window size is still vague and uncertain. Thus, in this study, we investigate the effect of different window size in segmentation process for common sports activity recognition. The study was conducted on ten subjects who wore a sensor from Gait Up called as Physilogic OR 4 Silver inertial sensor on their chest while performing several common sports activities such as stationary, walking, jogging, sprinting, and jumping. Three common used classifiers which are Decision Trees, k-Neighbor Nearest and Support Vector Machine were evaluated. Among the different ranges of window sizes tested, it was found that 2.5 seconds window size represents the best trade-off in recognition of common sports activity, with an obtained accuracy above 90%. From the result, it indicates that the selection of window size in segmentation process can affect the accuracy in detecting the common sports activity. The preferably employed window size in detecting the common sports activity is determined.
Author As'ari, Muhammad Amir
Ghazali, Nurul Fathiah
Latip, Hadafi Fitri Mohd
Shahar, Norazman
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  organization: Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
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Snippet Signal segmentation is one of the most important processes in the activity recognition process. So far, windowing approaches is one of the commonly used...
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SubjectTerms Activity recognition
classification
common sport activity recognition
Conferences
Data mining
Feature extraction
inertial sensor
Microsoft Windows
performance evaluation
segmentation
Sports
Support vector machines
Window size
Title Investigation on the Effect of Different Window Size in Segmentation for Common Sport Activity
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