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Related Concept Videos

Pain01:20

Pain

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Pain serves as a critical warning signal that alerts the body to potential or actual harm. When mechanical pressure on the skin is intense, such as from a sharp pinch, the sensation transitions from touch to pain. Similarly, extreme temperatures, like a hot pot handle, convert the sensation of heat into pain. Pain can also result from overstimulation of other senses, such as blinding light, loud noise, or the intense heat from habañero peppers. This ability to sense pain is essential for...
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Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
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Pain Detection using a Smartphone in Real Time.

Youngsun Kong, Hugo F Posada-Quintero, Ki H Chon

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    This study introduces an objective pain detection method using a smartphone and wearable device to measure electrodermal activity (EDA). The modified TVSymp method achieved 90% accuracy in detecting pain, outperforming the standard TVSymp method.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Human-Computer Interaction

    Background:

    • Existing pain detection methods often rely on subjective self-reports or less accurate smartphone camera analysis.
    • There is a need for objective, real-time pain assessment tools.

    Purpose of the Study:

    • To develop and validate an objective, real-time pain detection system using electrodermal activity (EDA) signals.
    • To compare the efficacy of two EDA analysis methods: TVSymp and modified TVSymp (MTVSymp).

    Main Methods:

    • Utilized a wrist-worn wearable device to collect EDA signals, transmitting data via Bluetooth to a smartphone application.
    • Developed a smartphone app to analyze EDA data using time-varying spectral power (TVSymp) and a modified approach (MTVSymp) that accounts for baseline fluctuations.
    • Induced pain using a thermal grill in ten subjects and compared the pain detection accuracy of TVSymp and MTVSymp via machine learning.

    Main Results:

    • The modified TVSymp (MTVSymp) method achieved a pain detection accuracy of 90%.
    • The standard TVSymp method demonstrated a pain detection accuracy of 80%.
    • MTVSymp showed superior performance in objectively detecting pain compared to TVSymp.

    Conclusions:

    • The developed smartphone-based system with MTVSymp offers a promising objective and accurate method for real-time pain detection.
    • This technology has potential applications in pain management and assessment, improving upon current subjective or less accurate methods.