A Data-Science Approach for Creation of a Comprehensive Model to Assess the Impact of Mobile Technologies on Humans

2023 · Harm · Low evidence

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HarmLow evidencePaper 519

Study snapshot

Publicationmanual · 2023

Study typeother

Reported directionHarm

Evidence ratingLow

Abstract

A Data-Science Approach for Creation of a Comprehensive Model to Assess the Impact of Mobile Technologies on Humans Garvanova M, Garvanov I, Jotsov V, Razaque A, Alotaibi B, Alotaibi M, Borissova D. A Data-Science Approach for Creation of a Comprehensive Model to Assess the Impact of Mobile Technologies on Humans. Applied Sciences. 2023; 13(6):3600. doi.org. Abstract Mobile technologies are an essential part of people’s everyday lives since they are utilized for a variety of purposes, such as communication, entertainment, commerce, and education. However, when these gadgets are misused, the human body is exposed to continuous radiation from the electromagnetic field created by them. The communication services available are improving as mobile technologies advance; however, the problem is becoming more severe as the frequency range of mobile devices expands. To solve this complex case, it is necessary to propose a comprehensive approach that combines and processes data obtained from different types of research and sources of information, such as thermal imaging, electroencephalograms, computer models, and surveys. In the present article, a complex model for the processing and analysis of heterogeneous data is proposed based on mathematical and statistical methods in order to study the problem of electromagnetic radiation from mobile devices in-depth. Data science selection/preprocessing is one of the most important aspects of data and knowledge processing aiming at successful and effective analysis and data fusion from many sources. Special types of logic-based binding and pointing constraints are considered for data/knowledge selection applications. The proposed logic-based statistical modeling method provides both algorithmic as well as data-driven realizations that can be evolutionary. As a result, non-anticipated and collateral data/features can be processed if their role in the selected/constrained area is significant. In this research, the data-driven part does not use artificial neural networks; however, this combination was successfully applied in the past. It is an independent subsystem maintaining control of both the statistical and machine-learning parts. The proposed modeling applies to a wide range of reasoning/smart systems. Conclusions We developed a novel data-science technique to identify the detrimental impacts of electromagnetic radiation from mobile devices on the human body. The proposed method for analyzing heterogeneous data is based on mathematical and statistical methodologies (thermal imaging and electroencephalograms). The proposed solution combines the ANOVA statistical method with deep modeling and rapid preprocessing approaches, such as binding/pointing/crossword constraints. Several tests were conducted utilizing the Pwelch function of MATLAB software, both with and without a mobile device. Each experiment was 30 min long. The resulting signals were filtered and classified into four frequency ranges: delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–32 Hz). The accuracy was determined for each frequency range with and without a mobile device based on the collected signals. The findings demonstrate that the emission of electromagnetic radiation from mobile devices has an effect on the signal frequency range accuracy. The presence of irradiation leads to an increase in the amplitude of brain signals in different frequency ranges. Furthermore, the results show that improved accuracy was reached without the use of a mobile device for each frequency band. The proposed approach has limitations because it increases the computational complexity due to obtaining heterogeneous data. However, this issue can be resolved using a data-mining approach. In the future, different Quality-of-Service parameters (e.g., energy consumption, time complexity, and reliability) will be examined in the future. Furthermore, the proposed approach will also be compared to state-of-the-art approaches: IoT based mobile monitoring framework for hyper-local PM2 [1], and cognitive emotion pre-occupation method [38]. Open access paper: mdpi.com

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AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeother

Effect directionharm

PopulationNot supplied

Sample sizeNot supplied

ExposureRF · 30 min per experiment

Evidence strengthLowConfidence: 66% · Peer reviewed: yes

Main findings

The paper proposes a data-science/statistical modeling approach to analyze heterogeneous data (including EEG and thermal imaging) to study electromagnetic radiation from mobile devices. Based on tests using MATLAB Pwelch with and without a mobile device (30 minutes each), the authors report increased EEG signal amplitudes in multiple frequency ranges in the presence of irradiation and higher classification accuracy without the mobile device.

Outcomes measured

EEG signal amplitude across delta/theta/alpha/beta bands
Signal frequency-range classification accuracy (with vs without mobile device)

Limitations

Computational complexity is increased due to obtaining and processing heterogeneous data.
Key experimental details (e.g., participant characteristics, sample size, exposure parameters) are not provided in the abstract.

View raw extracted JSON

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  "publication_year": 2023,
  "study_type": "other",
  "exposure": {
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    "source": "mobile phone",
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    "duration": "30 min per experiment"
  },
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  "sample_size": null,
  "outcomes": [
    "EEG signal amplitude across delta/theta/alpha/beta bands",
    "Signal frequency-range classification accuracy (with vs without mobile device)"
  ],
  "main_findings": "The paper proposes a data-science/statistical modeling approach to analyze heterogeneous data (including EEG and thermal imaging) to study electromagnetic radiation from mobile devices. Based on tests using MATLAB Pwelch with and without a mobile device (30 minutes each), the authors report increased EEG signal amplitudes in multiple frequency ranges in the presence of irradiation and higher classification accuracy without the mobile device.",
  "effect_direction": "harm",
  "limitations": [
    "Computational complexity is increased due to obtaining and processing heterogeneous data.",
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  "stance": "concern",
  "stance_confidence": 0.72,
  "summary": "This article presents a data-science framework for processing heterogeneous data sources (e.g., EEG, thermal imaging, surveys, and models) to assess impacts of electromagnetic radiation from mobile devices. In reported tests using MATLAB Pwelch with and without a mobile device (30 minutes per experiment), the authors state that irradiation increased EEG signal amplitudes across multiple frequency bands and reduced frequency-range classification accuracy compared with the no-device condition. The abstract does not provide key details such as sample size, participant characteristics, or exposure metrics.",
  "key_points": [
    "The study proposes a comprehensive modeling approach to fuse heterogeneous data for assessing mobile-device electromagnetic radiation effects.",
    "Experiments were conducted with and without a mobile device, with each experiment lasting 30 minutes.",
    "EEG signals were filtered and classified into delta, theta, alpha, and beta frequency ranges.",
    "The authors report increased EEG signal amplitudes in different frequency ranges when irradiation was present.",
    "The authors report better frequency-range classification accuracy without the mobile device across all bands.",
    "The approach is described as computationally complex due to heterogeneous data acquisition and processing.",
    "The abstract does not specify sample size, participant demographics, or exposure characteristics such as frequency or SAR."
  ],
  "categories": [
    "Mobile Phones",
    "RF Exposure",
    "Neurophysiology (EEG)",
    "Methods & Modeling"
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  "tags": [
    "Mobile Devices",
    "Electromagnetic Radiation",
    "Radiofrequency Exposure",
    "Electroencephalogram",
    "Thermal Imaging",
    "Data Fusion",
    "Statistical Modeling",
    "ANOVA",
    "MATLAB Pwelch",
    "Frequency Bands",
    "Signal Classification"
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    "heterogeneous data",
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    "ANOVA",
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  "social": {
    "tweet": "A 2023 Applied Sciences paper proposes a data-science framework to fuse heterogeneous data (e.g., EEG, thermal imaging) to assess mobile-device EMR impacts. In 30-min tests with vs without a phone, authors report higher EEG amplitudes and lower classification accuracy with irradiation.",
    "facebook": "A 2023 open-access paper in Applied Sciences presents a data-science approach for combining heterogeneous data (including EEG and thermal imaging) to study electromagnetic radiation from mobile devices. In 30-minute tests with and without a phone, the authors report increased EEG amplitudes across frequency bands and better signal classification accuracy without the device.",
    "linkedin": "Applied Sciences (2023) describes a data-science/statistical modeling framework for heterogeneous data fusion (EEG, thermal imaging, surveys, models) to assess mobile-device EMR. Reported 30-min tests with vs without a phone suggest increased EEG amplitudes and reduced frequency-band classification accuracy under irradiation."
  }
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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.3390/app13063600
PubMed: Not supplied

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