Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs Articles uri icon

publication date

  • June 2019

start page

  • 74422

end page

  • 74436


  • 7

International Standard Serial Number (ISSN)

  • 2169-3536

Electronic International Standard Serial Number (EISSN)

  • 2169-3536


  • Wearable sensors provide a user-friendly and non-intrusive
    mechanism to extract user-relateddata
    that paves the way to the development of personalized applications. Within
    those applications, humanactivity
    recognition (HAR) plays an important role in the characterization of the user
    context. Outlierdetection
    methods focus on finding anomalous data samples that are likely to have been
    generated by adifferent
    mechanism. This paper combines outlier detection and HAR by introducing a novel
    algorithmthat is able both to
    detect information from secondary activities inside the main activity and to
    extract datasegments of a
    particular sub-activity from a different activity. Several machine learning
    algorithms havebeen previously
    used in the area of HAR based on the analysis of the time sequences generated
    by wearablesensors. Deep
    recurrent neural networks (DRNNs) have proven to be optimally adapted to the
    sequentialcharacteristics of
    wearable sensor data in previous studies. A DRNN-based algorithm is proposed in
    thispaper for outlier
    detection in HAR. The results are validated both for intra- and inter-subject
    cases and bothfor outlier
    detection and sub-activity recognition using two different datasets. A first
    dataset comprising4 major
    activities (walking, running, climbing up, and down) from 15 users is used to
    train and validatethe
    proposal. Intra-subject outlier detection is able to detect all major outliers
    in the walking activity in thisdataset,
    while inter-subject outlier detection only fails for one participant executing
    the activity in a peculiarway.
    Sub-activity detection has been validated by finding out and extracting walking
    segments present inthe other
    three activities in this dataset. A second dataset using four different users,
    a different setting anddifferent
    sensor devices is used to assess the generalization of results.


  • Telecommunications


  • human activity recognition; wearable sensors; outlier detection; machine learning; deeplearning; recurrent neural networks; lstms