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Comparing CNN and Human Crafted Features for Human Activity Recognition
Deep learning techniques such as Convolutional
Neural Networks (CNNs) have shown good results in activity
recognition. One of the advantages of using these methods resides
in their ability to generate features automatically. This ability
greatly simplifies the task of feature extraction that usually
requires domain specific knowledge, especially when using big
data where data driven approaches can lead to anti-patterns.
Despite the advantage of this approach, very little work has
been undertaken on analyzing the quality of extracted features,
and more specifically on how model architecture and parameters
affect the ability of those features to separate activity classes
in the final feature space. This work focuses on identifying the
optimal parameters for recognition of simple activities applying
this approach on both signals from inertial and audio sensors.
The paper provides the following contributions: (i) a comparison
of automatically extracted CNN features with gold standard
Human Crafted Features (HCF) is given, (ii) a comprehensive
analysis on how architecture and model parameters affect separation
of target classes in the feature space. Results are evaluated
using publicly available datasets. In particular, we achieved a
93.38% F-Score on the UCI-HAR dataset, using 1D CNNs with
3 convolutional layers and 32 kernel size, and a 90.5% F-Score
on the DCASE 2017 development dataset, simplified for three
classes (indoor, outdoor and vehicle), using 2D CNNs with 2
convolutional layers and a 2x2 kernel size
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