Window Selection Impact in Human Activity Recognition

  • Nurul Nurwulan Department of Industrial Engineering, Sampoerna University, Jakarta, Indonesia
  • Bernard C. Jiang Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan
Keywords: Activity recognition, segmentation, windowing, window type, human movement


Signal segmentation is usually applied in the pre-processing step to make the data analysis easier. Windowing approach is commonly used for signal segmentation. However, it is unclear which type of window should be used to get optimum accuracy in human activity recognition. This study aimed to evaluat e which window type yields the optimum accuracy in human activity recognition. The acceleration data of walking, jogging, and running were collected from 20 young adults. Then, the recognition accuracy of each window types is evaluated and compared to determine the impact of window selection in human movement data. From the evaluation, the overlapping 75% window with 0.1 s length provides the highest accuracy with mean, standard deviation, maximum, minimum, and energy as the features. The result of this study could be used for future researches in relation to human activity recognition. 


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How to Cite
Nurwulan, N., & Jiang, B. C. (2020). Window Selection Impact in Human Activity Recognition. International Journal of Innovative Technology and Interdisciplinary Sciences, 3(1), 381-394.