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Methodology of Activity Recognition: Features and Learning Methods

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IoT Sensor-Based Activity Recognition

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

Abstract

Sensor-based Human Activity Recognition (HAR) has been explored by many research communities and industries for various applications. Conventional pattern recognition approaches based on handcrafted features contributed a lot in this research field by employing general classification approaches. This chapter represents those handcrafted features in time and frequency domain along with their importance and feature selection methods. Following these methods, this chapter provides explanations of several conventional machine learning techniques for classifying sensor data for activity recognition. The problems of overfitting and underfitting have been discussed with remedies. Previous research works using conventional pattern recognition (PR) approaches on some benchmark datasets have also been analyzed.

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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). Methodology of Activity Recognition: Features and Learning Methods. In: IoT Sensor-Based Activity Recognition. Intelligent Systems Reference Library, vol 173. Springer, Cham. https://doi.org/10.1007/978-3-030-51379-5_3

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