Journal article Open Access

Child Activity Recognition using Deep Learning

Binjal Suthar; Bijal Gadhiya

MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="">
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">CNN, Deep Learning, Child Activity Recognition.</subfield>
  <controlfield tag="005">20211004134830.0</controlfield>
  <controlfield tag="001">5547041</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Assistant professor in Computer Engineering  Department, at Government Engineering College, Gandhinagar.</subfield>
    <subfield code="a">Bijal Gadhiya</subfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Publisher</subfield>
    <subfield code="4">spn</subfield>
    <subfield code="a">Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)</subfield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">501451</subfield>
    <subfield code="z">md5:fceaf9a4b225a38757233f82d58fa5e7</subfield>
    <subfield code="u"></subfield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-06-30</subfield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="o"></subfield>
  <datafield tag="909" ind1="C" ind2="4">
    <subfield code="c">364-367</subfield>
    <subfield code="n">5</subfield>
    <subfield code="p">International Journal of Engineering and Advanced Technology (IJEAT)</subfield>
    <subfield code="v">9</subfield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Department of Software Engineering from Government  Engineering College, Gandhinagar, Gujarat</subfield>
    <subfield code="a">Binjal Suthar</subfield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Child Activity Recognition using Deep Learning</subfield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u"></subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2"></subfield>
  <datafield tag="650" ind1="1" ind2=" ">
    <subfield code="a">ISSN</subfield>
    <subfield code="0">(issn)2249-8958</subfield>
  <datafield tag="650" ind1="1" ind2=" ">
    <subfield code="a">Retrieval Number</subfield>
    <subfield code="0">(handle)E9563069520/2020©BEIESP</subfield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;The human action recognition is the subject to predicting what an individual is performing based on a trace of their development exploiting a several strategies. Perceiving human activities is an ordinary region of eagerness in view of its various potential applications; though, it is still in start. It is a trending analysis area possessed by the range from dependable automation, medicinal services to developing the smart supervision system. In this work, we are trying to recognize the activity of the child from video dataset using deep learning techniques. The proposed system will help parent to take care of their baby during the job or from anywhere else to know what the baby is doing. This can also be useful to prevent the in-house accident falls of the child and for health monitoring. The activities can be performed by child include sleeping, walking, running, crawling, playing, eating, cruising, clapping, laughing, crying and many more. We are focusing on recognizing crawling, running, sleeping, and walking activities of the child in this study. The offered system gives the best result compared with the existing methods, which utilize sensor-based information. Experimental results proved that the offered deep learning model had accomplished 94.73% accuracy for recognizing the child activity.&lt;/p&gt;</subfield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">issn</subfield>
    <subfield code="i">isCitedBy</subfield>
    <subfield code="a">2249-8958</subfield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.35940/ijeat.E9563.069520</subfield>
    <subfield code="2">doi</subfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">article</subfield>
Views 34
Downloads 34
Data volume 17.0 MB
Unique views 34
Unique downloads 32


Cite as