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Related Concept Videos

IR Spectrum01:19

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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
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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...
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Related Experiment Video

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Radio frequency-based human activity dataset collected using ESP32 microcontroller in line-of-sight and

Zhe-Yu Lim1, Lee-Yeng Ong1, Meng-Chew Leow1

  • 1Faculty of Information Science and Technology, Multimedia University Melaka Campus, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia.

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|December 5, 2024
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Summary

The ESP32 Dataset offers radio frequency data for human activity detection, featuring 1,600 trials across diverse indoor scenarios. This resource aids in developing efficient algorithms for Internet of Things applications.

Keywords:
Channel state information (CSI)Human activity detectionLine-of-sight (LOS)Non-line-of-sight (NLOS)Radio frequencyReceived signal strength indicator (RSSI)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Human activity detection is crucial for smart environments and security.
  • Existing radio frequency (RF) datasets often lack diversity in experimental setups or hardware specificity.
  • The Internet of Things (IoT) demands resource-efficient algorithms for real-time applications.

Purpose of the Study:

  • To introduce the ESP32 Dataset, a novel RF dataset for human activity detection.
  • To provide a rich and diversified dataset encompassing various activities and indoor environments.
  • To facilitate the development of resource-efficient activity detection algorithms tailored for ESP32 microcontrollers in IoT.

Main Methods:

  • Collected radio frequency (RF) data using ESP32 microcontrollers as receivers and a D-Link AX3000 router as a transmitter.
  • Recorded Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) for 10 human activities performed by 8 volunteers.
  • Designed three experiment setups simulating line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, with 20 trials per activity per volunteer.

Main Results:

  • The ESP32 Dataset comprises 1,600 recorded trials across different setups and activities.
  • The dataset captures one RSSI value and fifty-two CSI subcarriers per transmission, specific to ESP32 hardware.
  • The data includes detailed RF signal characteristics suitable for algorithm development and evaluation.

Conclusions:

  • The ESP32 Dataset is a valuable resource for researchers and practitioners in human activity detection.
  • The dataset's specificity to ESP32 hardware enables the creation of optimized, low-power algorithms for IoT.
  • This dataset supports the advancement of activity detection methodologies and experimental setups in IoT environments.