Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Multi-Modal Representation Learning for Affective Computing
  with Multi-Corpus Wearable Data   With recent developments in smart technologies, there has been a growing
focus on the use of artificial intelligence and machine learning for affective
computing to further enhance the user experience through emotion recognition.
Typically, machine learning models used for affective computing are trained
using manually extracted features from biological signals. Such features may
not generalize well for large datasets and may be sub-optimal in capturing the
information from the raw input data. One approach to address this issue is to
use fully supervised deep learning methods to learn latent representations of
the biosignals. However, this method requires human supervision to label the
data, which may be unavailable or difficult to obtain. In this work we propose
an unsupervised framework reduce the reliance on human supervision. The
proposed framework utilizes two stacked convolutional autoencoders to learn
latent representations from wearable electrocardiogram (ECG) and electrodermal
activity (EDA) signals. These representations are utilized within a random
forest model for binary arousal classification. This approach reduces human
supervision and enables the aggregation of datasets allowing for higher
generalizability. To validate this framework, an aggregated dataset comprised
of the AMIGOS, ASCERTAIN, CLEAS, and MAHNOB-HCI datasets is created. The
results of our proposed method are compared with using convolutional neural
networks, as well as methods that employ manual extraction of hand-crafted
features. The methodology used for fusing the two modalities is also
investigated. Lastly, we show that our method outperforms current
state-of-the-art results that have performed arousal detection on the same
datasets using ECG and EDA biosignals. The results show the wide-spread
applicability for stacked convolutional autoencoders to be used with machine
learning for affective computing.

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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph