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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
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Updated: Jun 22, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
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Real-time human identification using a pyroelectric infrared detector array and hidden Markov models.

Jian-Shuen Fang, Qi Hao, David J Brady

    Optics Express
    |June 12, 2009
    PubMed
    Summary

    This study introduces a real-time human identification system using pyroelectric infrared (PIR) sensors and hidden Markov models (HMMs). The system effectively recognizes individuals based on their unique motion features captured by PIR sensors.

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    Area of Science:

    • Biometrics
    • Signal Processing
    • Machine Learning

    Background:

    • Traditional human identification methods face challenges in real-world scenarios.
    • The need for non-intrusive, real-time identification systems is growing.

    Purpose of the Study:

    • To propose and verify a novel real-time human identification system.
    • To leverage pyroelectric infrared (PIR) detector arrays and hidden Markov models (HMMs) for motion-based identification.

    Main Methods:

    • Utilizing a PIR detector array with masked Fresnel lens arrays to capture human motion features as digital sequential data.
    • Training HMMs using an expectation-maximization (EM) algorithm to model individual motion patterns.
    • Employing the maximum-likelihood (ML) criterion for recognizing human subjects based on new feature data.

    Main Results:

    • A prototype system was successfully developed and tested.
    • The system demonstrated the capability for real-time human identification based on motion features.
    • Optimization of sensor modules (detector count, sampling masks) was explored to enhance identification accuracy.

    Conclusions:

    • The proposed PIR sensor and HMM-based system offers a viable solution for real-time human identification.
    • The method effectively models and distinguishes individuals through their unique motion signatures.
    • Further sensor configuration testing can improve system performance.