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Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark

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Deep Learning for Human Activity Recognition (DL-HAR 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1370))

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Abstract

Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.

R. Abdel-Salam and R. Mostafa—Equal Contribution.

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Notes

  1. 1.

    Recent works are implemented using the same architecture and hyper-parameters as mentioned in their papers and re-evaluated using proposed standardized benchmark.

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Acknowledgments

We would like to thank Jordao et al. [12] for sharing datasets: MHealth, USC-HAD, UTD-MHAD, WHARF, and WISDM that have been segmented by the temporal window generation techniques publicly to the community.

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Authors and Affiliations

  1. Department of Computer Engineering, Cairo University, Giza, Egypt

    Reem Abdel-Salam, Rana Mostafa & Mayada Hadhood

Authors
  1. Reem Abdel-Salam
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  2. Rana Mostafa
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  3. Mayada Hadhood
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Corresponding authors

Correspondence to Reem Abdel-Salam or Rana Mostafa .

Editor information

Editors and Affiliations

  1. Institute for Infocomm Research, A*STAR, Singapore, Singapore

    Xiaoli Li

  2. Institute for Infocomm Research, A*STAR, Singapore, Singapore

    Min Wu

  3. Institute for Infocomm Research, A*STAR, Singapore, Singapore

    Zhenghua Chen

  4. Institute for Infocomm Research, A*STAR, Singapore, Singapore

    Le Zhang

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Abdel-Salam, R., Mostafa, R., Hadhood, M. (2021). Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark. In: Li, X., Wu, M., Chen, Z., Zhang, L. (eds) Deep Learning for Human Activity Recognition. DL-HAR 2021. Communications in Computer and Information Science, vol 1370. Springer, Singapore. https://doi.org/10.1007/978-981-16-0575-8_1

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  • DOI: https://doi.org/10.1007/978-981-16-0575-8_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0574-1

  • Online ISBN: 978-981-16-0575-8

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