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Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

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Advances in Computer Vision (CVC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 944))

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Abstract

Physical activity recognition using wearable devices can provide valued information regarding an individual’s degree of functional ability and lifestyle. Smartphone-based physical activity recognition is a well-studied area. However, research on smartwatch-based physical activity recognition, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based physical activity recognition domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based physical activity recognition system for both personal and impersonal models in real life scenarios. To further validate our hypothesis for both personal and impersonal models, we tested single subject out cross validation process for smartwatch-based physical activity recognition.

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Authors and Affiliations

  1. Innopolis University, Innopolis, Kazan, Russia

    Muhammad Ahmad, Adil Khan & Manuel Mazzara

  2. University of Messina, Messina, Italy

    Muhammad Ahmad & Salvatore Distefano

Authors
  1. Muhammad Ahmad
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  2. Adil Khan
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  3. Manuel Mazzara
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  4. Salvatore Distefano
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Corresponding author

Correspondence to Muhammad Ahmad .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Ahmad, M., Khan, A., Mazzara, M., Distefano, S. (2020). Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_19

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