Scaling Activity Recognition Using Channel State Information Through Convolutional Neural Networks and Transfer Learning

Jeroen Klein Brinke*, Nirvana Meratnia

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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    Abstract

    Unobtrusive sensing is receiving much attention in recent years, as it is less obtrusive and more privacy-aware compared to other monitoring technologies. Human activity recognition is one of the fields in which unobtrusive sensing is heavily researched„ as this is especially important in health care. In this regard, investigating WiFi signals, and more specifically 802.11n channel state information, is one of the more prominent research fields. However, there is a challenge in scaling it up. Transfer learning is rarely applied, and when applied, it is done on filtered/modified data or extracted features. This paper focuses on two aspects. First, convolutional networks are used across multiple participants, days and activities and analysis is done based on these results. Secondly, it looks into the possibility of applying transfer learning based on raw channel state information over multiple participants and activities over multiple days. Results show channel state information is accurate for single participants (F1-score of 0.90), but sensitive to different participants and fluctuating WiFi signals over days (F1-score of 0.25-0.35). Furthermore, results show both clustering and transfer learning can be applied to increase the performance to 0.80 when using minimal resources and retraining.
    Original languageEnglish
    Title of host publicationAIChallengeIoT ’19
    Subtitle of host publicationInternational Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things
    Place of PublicationNew York, NY
    Pages56-62
    Number of pages7
    DOIs
    Publication statusPublished - 2019
    Event1st International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, AIChallengeIoT 2019 - New York, United States
    Duration: 10 Nov 201913 Nov 2019
    Conference number: 1

    Conference

    Conference1st International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, AIChallengeIoT 2019
    Abbreviated titleAIChallengeIoT
    CountryUnited States
    CityNew York
    Period10/11/1913/11/19

    Keywords

    • Datasets
    • Channel state information
    • Human activity recognition
    • Remote sensing
    • Deep learning
    • Convolutional neural networks
    • Transfer learning

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  • Cite this

    Klein Brinke, J., & Meratnia, N. (2019). Scaling Activity Recognition Using Channel State Information Through Convolutional Neural Networks and Transfer Learning. In AIChallengeIoT ’19: International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (pp. 56-62). New York, NY. https://doi.org/10.1145/3363347.3363362