Real time human activity recognition on smartphones using LSTM networks
Date Issued
2018-05-21
Author(s)
Risteska Stojkoska, Biljana
Abstract
Activity detection is becoming an integral part of
many mobile applications. Therefore, the algorithms for this
purpose should be lightweight to operate on mobile or other
wearable device, but accurate at the same time. In this paper, we
develop a new lightweight algorithm for activity detection based
on Long Short Term Memory networks, which is able to learn
features from raw accelerometer data, completely bypassing the
process of generating hand-crafted features. We evaluate our
algorithm on data collected in controlled setting, as well as on data
collected under field conditions, and we show that our algorithm
is robust and performs almost equally good for both scenarios,
while outperforming other approaches from the literature.
many mobile applications. Therefore, the algorithms for this
purpose should be lightweight to operate on mobile or other
wearable device, but accurate at the same time. In this paper, we
develop a new lightweight algorithm for activity detection based
on Long Short Term Memory networks, which is able to learn
features from raw accelerometer data, completely bypassing the
process of generating hand-crafted features. We evaluate our
algorithm on data collected in controlled setting, as well as on data
collected under field conditions, and we show that our algorithm
is robust and performs almost equally good for both scenarios,
while outperforming other approaches from the literature.
Subjects
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