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Basic Structure for Human Activity Recognition Systems: Preprocessing and Segmentation

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IoT Sensor-Based Activity Recognition

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 173))

Abstract

Automatic recognition of human activities using sensor-based systems is commonly known as human activity recognition (HAR). It is required to follow a structural pipeline to recognize activity using a machine learning technique. This chapter represents the different stages of this structural pipeline in detail. Following this, the preprocessing steps have been analyzed to clean and remove noises from raw sensor data. The importance of segmentation and criterions to select the best windowing method have been also described based on previous research works. The challenges regarding the selection of window length, window type, choosing overlapping percentage, and the relation between window duration and performance have been also investigated in the end.

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Authors and Affiliations

  1. Osaka University, Osaka, Japan

    Md Atiqur Rahman Ahad

  2. University of Dhaka, Dhaka, Bangladesh

    Md Atiqur Rahman Ahad

  3. University of Michigan, Ann Arbor, Michigan, USA

    Anindya Das Antar

  4. University of Maryland Baltimore County, Maryland, USA

    Masud Ahmed

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  1. Md Atiqur Rahman Ahad
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  2. Anindya Das Antar
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  3. Masud Ahmed
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Correspondence to Md Atiqur Rahman Ahad .

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Ahad, M.A.R., Antar, A.D., Ahmed, M. (2021). Basic Structure for Human Activity Recognition Systems: Preprocessing and Segmentation. In: IoT Sensor-Based Activity Recognition. Intelligent Systems Reference Library, vol 173. Springer, Cham. https://doi.org/10.1007/978-3-030-51379-5_2

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