{
  "abstract": "Safety has always been a concern. Thus, employing and utilizing technology is vital to help society address this concern. This paper looks at the design and implementation of a Smart Safety Protection System (SSPS), which employs the Internet of Things (IoT) to solve the issue of real-time monitoring, geolocation tracking, and emergency alert features. The proposed system is a wearable device that uses sensors and GPS to send continuous data to both an application on smartphones and a database on the cloud. The system’s features include detecting unauthorized removal of the device as well as falls and abnormal temperature or heart rate. If anything happens as mentioned, the system will send alerts to the linked mobile application. A Deep Learning (DL) model is integrated into the SSPS system using multimodal WESAD sensor data. A trained Multi-Layer Perceptron (MLP) neural network achieved 93% accuracy when optimized with Adam and Softmax, enabling reliable identification of Monitored Individuals’ emotional states. Steps of functional and integration testing of the prototype proved the reliability of sensor accuracy, data transmission speed, and real-time emergency response. Ultimately, this work emphasizes the potential of using DL and the IoT to improve Monitored Individuals’ safety.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia",
    "Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia",
    "Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia",
    "Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia",
    "Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kaouther Omri"
    },
    {
      "affs": [
        1
      ],
      "name": "Hanen Himdi"
    },
    {
      "affs": [
        2
      ],
      "name": "Manal M. Khayyat"
    },
    {
      "affs": [
        3
      ],
      "name": "Ala’ A. Eshmawi"
    },
    {
      "affs": [
        4
      ],
      "name": "Mashael M. Khayyat"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/98n928frw093n.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0354273",
  "fulltext_url": null,
  "id": "98n928frw093n",
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    "1932-6203"
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Context-Aware Activity Recognition Systems"
    ],
    [
      "plos",
      "/Biology and life sciences/Cell biology/Signal transduction/Cell signaling/Signaling cascades/Stress signaling cascade"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Emotions"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
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    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Engineering and technology/Technology development/Prototypes"
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      "/Medicine and health sciences/Cardiology/Heart rate"
    ],
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      "plos",
      "/Medicine and health sciences/Health care/Caregivers"
    ],
    [
      "plos",
      "/Research and analysis methods/Bioassays and physiological analysis/Electrophysiological techniques/Cardiac electrophysiology/Electrocardiography"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Emotions"
    ]
  ],
  "openalex": "W7211974616",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0354273&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/98n928frw093n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0354273",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0354273"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:cell biology",
      "subfield": "cell-biology"
    },
    {
      "field": "medicine",
      "origin": "plos:cardiology",
      "subfield": "cardiovascular"
    }
  ],
  "title": "The implementation of a smart safety protection system using DL and IoT technologies",
  "title_text": "The implementation of a smart safety protection system using DL and IoT technologies",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/98n928frw093n",
  "venue": "PLOS One",
  "versions": []
}