Deep Learning Human Activity Recognition
Publication Type:
Refereed Conference Meeting Proceeding
Abstract:
Human activity recognition is an area of interest in various
domains such as elderly and health care, smart-buildings and surveillance,
with multiple approaches to solving the problem accurately and
efficiently. For many years hand-crafted features were manually extracted
from raw data signals, and activities were classed using support vector
machines and hidden Markov models. To further improve on this method
and to extract relevant features in an automated fashion, deep learning
methods have been used. The most common of these methods are Long
Short-Term Memory models (LSTM), which can take the sequential nature
of the data into consideration and outperform existing techniques,
but which have two main pitfalls; longer training times and loss of distant
pass memory. A relevantly new type of network, the Temporal Convolutional
Network (TCN), overcomes these pitfalls, as it takes significantly
less time to train than LSTMs and also has a greater ability to capture
more of the long term dependencies than LSTMs. When paired with
a Convolutional Auto-Encoder (CAE) to remove noise and reduce the
complexity of the problem, our results show that both models perform
equally well, achieving state-of-the-art results, but when tested for robustness
on temporal data the TCN outperforms the LSTM. The results
also show, for industry applications, the TCN can accurately be used for
Conference Name:
27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science
Proceedings:
http://ceur-ws.org
Digital Object Identifer (DOI):
10.NA
Publication Date:
05/12/2019
Conference Location:
Ireland
Research Group:
Institution:
National University of Ireland, Cork (UCC)
Open access repository:
Yes
Publication document: