TELKOMNIKA Telecommunication, Computing, Electronics and Control
Human activity recognition for static and dynamic activity using convolutional neural network
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Human activity recognition for static and dynamic activity using convolutional neural network
Human activity recognition for static and dynamic activity using convolutional neural network
Subject
Accelerometer
CNN
Convolution matrix
Gyroscope
Human activity recognition
Hyperparameter
CNN
Convolution matrix
Gyroscope
Human activity recognition
Hyperparameter
Description
Evaluated activity as a detail of the human physical movement has become a
leading subject for researchers. Activity recognition application is utilized in
several areas, such as living, health, game, medical, rehabilitation, and other
smart home system applications. An accelerometer was popular sensors to
recognize the activity, as well as a gyroscope, which can be embedded in a
smartphone. Signal was generated from the accelerometer as a time-series data
is an actual approach like a human actifvity pattern. Motion data have acquired
in 30 volunteers. Dynamic actives (walking, walking upstairs, walking
downstairs) as DA and static actives (laying, standing, sitting) as SA were
collected from volunteers. SA and DA it's a challenging problem with the
different signal patterns, SA signals coincide between activities but with a
clear threshold, otherwise the DA signal is clearly distributed but with an
adjacent upper threshold. The proposed network structure achieves a
significant performance with the best overall accuracy of 97%. The result
indicated the ability of the model for human activity recognition purposes.
leading subject for researchers. Activity recognition application is utilized in
several areas, such as living, health, game, medical, rehabilitation, and other
smart home system applications. An accelerometer was popular sensors to
recognize the activity, as well as a gyroscope, which can be embedded in a
smartphone. Signal was generated from the accelerometer as a time-series data
is an actual approach like a human actifvity pattern. Motion data have acquired
in 30 volunteers. Dynamic actives (walking, walking upstairs, walking
downstairs) as DA and static actives (laying, standing, sitting) as SA were
collected from volunteers. SA and DA it's a challenging problem with the
different signal patterns, SA signals coincide between activities but with a
clear threshold, otherwise the DA signal is clearly distributed but with an
adjacent upper threshold. The proposed network structure achieves a
significant performance with the best overall accuracy of 97%. The result
indicated the ability of the model for human activity recognition purposes.
Creator
Agus Eko Minarno, Wahyu Andhyka Kusuma, Yoga Anggi Kurniawan
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Jul 9, 2021
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Agus Eko Minarno, Wahyu Andhyka Kusuma, Yoga Anggi Kurniawan, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Human activity recognition for static and dynamic activity using convolutional neural network,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/4360.
Human activity recognition for static and dynamic activity using convolutional neural network,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/4360.