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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Human Related-Health Actions Detection using Android Camera based on TensorFlow Object Detection API

Author 1: Fadwa Al-Azzoa
Author 2: Arwa Mohammed Taqia
Author 3: Mariofanna Milanovab

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.091002

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 10, 2018.

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Abstract: A new method to detect human health-related actions (HHRA) from a video sequence using an Android camera. The Android platform works not only to capture video images through its camera, but also to detect emergency actions. An application for HHRA is to help monitor unattended children, individuals with special needs or the elderly. The application has been investigating based on TensorFlow Object Detection Application Program Interface (API) technique with Android studio. This paper fundamentally focuses on the comparison, in terms of improving speed and detection accuracy. In this work, two promising new approaches for HHRA detection has been proposed: SSD Mobilenet and Faster RCNN Resnet models. The proposed approaches are evaluated on the NTU RGB+D dataset, which it knows as the present greatest publicly accessible 3D action recognition dataset. The dataset has been split into training and testing dataset. The total confidence scores detection quality (total mAP) for all the actions classes are 95.8% based on the SSD-Mobilenet model and 93.8% based on Faster-R-CNN-Resnet model. The detection process is achieved using two methods to evaluate the detection performance using Android camera (Galaxy S6) and using TensorFlow Object Detection Notebook in terms of accuracy and detection speed. Experimental results have demonstrated valuable improvements in terms of detection accuracy and efficiency for human health-related actions identification. The experiments have executed on Ubuntu 16.04LTS GTX1070 @ 2.80GHZ x8 system.

Keywords: Android camera; TensorFlow object detection API; emergency actions; detection accuracy

Fadwa Al-Azzoa, Arwa Mohammed Taqia and Mariofanna Milanovab, “Human Related-Health Actions Detection using Android Camera based on TensorFlow Object Detection API” International Journal of Advanced Computer Science and Applications(IJACSA), 9(10), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091002

@article{Al-Azzoa2018,
title = {Human Related-Health Actions Detection using Android Camera based on TensorFlow Object Detection API},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091002},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091002},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {10},
author = {Fadwa Al-Azzoa and Arwa Mohammed Taqia and Mariofanna Milanovab}
}


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