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Head-AR: Human Activity Recognition with Head-Mounted IMU Using Weighted Ensemble Learning

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Activity and Behavior Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 204))

Abstract

This paper describes the machine learning (ML) method Head-AR, which achieved the highest performance in a competition with 11 other algorithms and won the Emteq Activity Recognition challenge. The goal of the challenge was to recognize eight activities of daily life from a device mounted on the head, which provided data from a 3-axis IMU: accelerometer, gyroscope, and magnetometer. The challenge dataset was collected by four subjects, of which one subject was used as a test for the challenge evaluation. The method processes the stream of sensors data and recognizes one of the eight activities every two seconds. The method is based on weighted ensemble learning, which combines three models: (i) a dynamic time warping classification model, which analyzes raw accelerometer data; (ii) a classification model that uses expert features; (iii) and a classification model that uses features selected by a feature selection algorithm. To compute the final output, the predictions of the three models are combined using a novel weighing scheme. The method achieved an F1-score of 61.25% on the competition’s evaluation.

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Notes

  1. 1.

    Emteq Ltd: https://emteq.net.

  2. 2.

    https://sites.google.com/up.edu.mx/challenge-up-2019.

  3. 3.

    https://abc-research.github.oio/cook2020/.

  4. 4.

    https://github.com/simon2706/Emteq-ARC2019.

  5. 5.

    https://tsfresh.readthedocs.io/en/latest/text/list_of_features.html.

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Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, Skopje, North Macedonia

    Hristijan Gjoreski

  2. Jozef Stefan Institute & Jozef Stefan Postgraduate School, Ljubljana, Slovenia

    Ivana Kiprijanovska, Simon Stankoski, Stefan Kalabakov & Martin Gjoreski

  3. Emteq Ltd, Brighton, UK

    John Broulidakis & Charles Nduka

Authors
  1. Hristijan Gjoreski
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  2. Ivana Kiprijanovska
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  3. Simon Stankoski
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  4. Stefan Kalabakov
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  5. John Broulidakis
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  6. Charles Nduka
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  7. Martin Gjoreski
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Corresponding author

Correspondence to Hristijan Gjoreski .

Editor information

Editors and Affiliations

  1. University of Dhaka, Dhaka, Bangladesh

    Md Atiqur Rahman Ahad

  2. Kyushu Institute of Technology, Fukuoka, Japan

    Sozo Inoue

  3. Sussex University, Brighton, UK

    Daniel Roggen

  4. Tokyo University of Agriculture and Technology, Tokyo, Japan

    Kaori Fujinami

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Gjoreski, H. et al. (2021). Head-AR: Human Activity Recognition with Head-Mounted IMU Using Weighted Ensemble Learning. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-15-8944-7_10

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