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Exploiting the PIR Sensor Analog Behavior as Thermoreceptor: Movement Direction Classification Based on Spiking

Jose-Maria Guerrero-Rodriguez1, Maria-Angeles Cifredo-Chacon1, Clemente Cobos Sánchez1

  • 1Microelectronic Circuit Design Group, Engineering School, University of Cadiz, Campus Universitario de Puerto Real, Avda. Universidad de Cádiz, nº 10, CP 11519 Puerto Real, Cádiz, Spain.

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|July 14, 2023
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Summary
This summary is machine-generated.

This study presents novel neural processing architectures for pyroelectric infrared (PIR) sensors, enabling them to mimic biological neurons. The research demonstrates effective optical flow detection using these enhanced PIR sensors in a spiking neural network.

Keywords:
PIR passive infrared sensorhuman occupancy detectionoptical flowpeople detectionspiking neural network

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Area of Science:

  • Neuroscience and Robotics
  • Sensor Technology and Signal Processing

Background:

  • Pyroelectric infrared (PIR) sensors are cost-effective, low-power infrared detectors used in motion control and autonomous systems.
  • Existing PIR sensor applications often treat them as simple binary detectors, limiting their potential in complex intelligent systems.

Purpose of the Study:

  • To develop and validate novel neural processing architectures for PIR sensors.
  • To emulate biological neural functions (presynaptic and postsynaptic neurons) using PIR sensor data.
  • To apply these architectures for advanced applications like optical flow detection.

Main Methods:

  • Implemented an analog processing circuit mimicking the leaky integrate-and-fire model for PIR sensors to generate spiking rates proportional to IR stimuli.
  • Developed an embedded postsynaptic neuron architecture using a spiking neural network matrix and digital processing for sensor data integration.
  • Utilized a four-PIR sensor array as input for an optical flow detection case study.

Main Results:

  • Successfully demonstrated an analog approach to convert PIR sensor signals into spiking rates, emulating presynaptic neuron behavior.
  • Validated the postsynaptic neuron architecture's ability to process and retrieve analog information from spike trains, simulating an optic nerve.
  • Achieved effective optical flow detection using the proposed neural processing approach with a four-PIR sensor array.

Conclusions:

  • The presented neural processing architectures significantly enhance the capabilities of PIR sensors beyond simple motion detection.
  • This work validates the feasibility of using PIR sensors in spiking neural networks for complex sensory processing tasks.
  • The findings pave the way for more sophisticated autonomous intelligent devices, robotics, and sensor networks.