Publication: Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs
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2019-06-05
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IEEE
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Abstract
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.
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Keywords
Human activity recognition, Wearable sensors, Outlier detection, Machine learning, Deeplearning, Recurrent neural networks, LSTMs
Bibliographic citation
M. Munoz-Organero, "Outlier Detection in Wearable Sensor Data for Human Activity Recognition (HAR) Based on DRNNs," in IEEE Access, vol. 7, pp. 74422-74436, 2019