There are many types of technology capable of detecting human presence and the main ones in use are ultrasonic sensors, photoelectric sensors, thermal imaging, and PIR.
ULTRASONIC SENSORS
Ultrasonic sensors operate by emitting inaudible sound waves that bounce off objects and return to the sensor. The sensor is triggered when a moving object is hit by the waves which causes a difference in wave return time. They are susceptible to false triggers and require careful calibration [8].
PHOTOELECTRIC SENSORS
Photoelectric sensors operate by emitting a beam of either visible or infrared light that is reflected by a target on the wall across from the sensor. The sensor is triggered when an object crosses the beam and changes the quantity of light reflected. An alternate design has the light emitters and receivers on opposite walls without reflection that is triggered whenever the beam is interrupted. They are susceptible to miscounting in areas of high occupancy and must be recalibrated if the sensor becomes misaligned. They are significantly more expensive than PIR sensors [9].
THERMAL IMAGING
Thermal imaging operates by detecting infrared energy and converting the detected information into electronic signals that are processed to form a thermal image. The images can them be processed by software that identifies and counts humans within the image. The concept is simple but programming the software involves many challenges. The technology is the most expensive sensor type, uses the most energy to run, and requires the most extensive installation. The cameras are also sometimes viewed as invasive and thus have an attached stigma [10 & 11].
PYROELECTRIC INFRARED (PIR) SENSORS
PIR sensors are already an established technology in smart thermostats and the subject of attempts to incorporate them into high occupancy smart thermostats. They are well suited for occupancy detection, making them the subject of this project.
Pyroelectric, or Passive, Infrared (PIR) sensors operate by measuring Infrared Radiation (IR) of nearby heat-emitting sources. The body heat of a passing human is emitted with wavelengths of 9.4 μm which falls within the range of the sensor, 8−14 μm [12]. The sensor has two slots that detect IR, as shown in Figure 1.
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The sensor measures ambient IR to establish a baseline while at rest, and when a warm object moves through the detection zones of the slots, differentials form in the surface electric charge to generate an output signal like the one shown in Figure 2. The signal's path is shown in Figure 3. With current technology, the sensor needs about 2 seconds to reset after detecting an object [13]. PIR sensors only detect moving heat sources. Humans have a typical walking speed on 0.5 meters per second, which is within the detectable speed range [14].
A lens is placed in front of the slots to control the detection width, range, and sensitivity, which can widely vary. Detection angles can be up to 180 degrees and range can be over 10 meters. Fresnel lenses, shown in Figure 4, are used as opposed to smooth lenses, to decrease material consumption and production costs.
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PIR sensors are easily mass produced, small, and inexpensive (only a few dollars for the basic sensor), require little maintenance, have a low power consumption (about 1 μA while in standby), and have easily modified detection ranges and sensitivities [15]. These qualities in addition to their accuracy in detecting human presence led them to become the technology of choice for smart thermostats in buildings with low occupancy. PIR sensors are also the sensor best suited for the goal of incorporating occupancy detection in thermostats for high occupancy buildings despite difficulties that will be addressed in the page titled PIR Incorporation.
Click the link on the right to go to the next section, PIR Incoporation.
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Sources:
[8] "Ultrasonic Occupancy Sensor," [online], December 8, 2016, Graybar, website: http://www.graybar.com/applications/lighting/controls/occupancy-sensors/ultrasonic.
[9] "Introductory Guide to Sensors," [online], December 8, 2016, Keyonce Corporation, website: http://www.keyence.com/ss/products/sensor/sensorbasics/photoelectric/info/.
[10] Johnson, N., "A smart sensor to detect the falls of the elderly," IEEE Pervasive Computing, 3 (2), pp. 42-47 (2004).
[11] Nanda, H., and Davis, L., "Probabilistic template based pedestrian detection in infrared videos," presented at the Intelligent Vehicle Symposium (2002).
[12] Moghavvemi, M., and Seng, L. U., "Pyroelectric infrared sensor for intruder detection," presented at the 2004 IEEE Region 10 Conference (November 24, 2004).
[13] "PIR Motion Sensor," [online], December 8, 2016, Adafruit, website: https://learn.adafruit.com/pir-passive-infrared-proximity-motion-sensor/how-pirs-work.
[14] Shankar, M., et al, "Human-tracking systems using pyroelectric infrared detectors," Optical Engineering, 45 (10), (2006).
[15] Wahl, F., Milenkovic, M., Amft, O., "A distributed PIR based approach for estimating people count in office environments," presented at the 2012 IEEE 15th International Computational Science and Engineering, Washington DC, US, (December 2012).
[8] "Ultrasonic Occupancy Sensor," [online], December 8, 2016, Graybar, website: http://www.graybar.com/applications/lighting/controls/occupancy-sensors/ultrasonic.
[9] "Introductory Guide to Sensors," [online], December 8, 2016, Keyonce Corporation, website: http://www.keyence.com/ss/products/sensor/sensorbasics/photoelectric/info/.
[10] Johnson, N., "A smart sensor to detect the falls of the elderly," IEEE Pervasive Computing, 3 (2), pp. 42-47 (2004).
[11] Nanda, H., and Davis, L., "Probabilistic template based pedestrian detection in infrared videos," presented at the Intelligent Vehicle Symposium (2002).
[12] Moghavvemi, M., and Seng, L. U., "Pyroelectric infrared sensor for intruder detection," presented at the 2004 IEEE Region 10 Conference (November 24, 2004).
[13] "PIR Motion Sensor," [online], December 8, 2016, Adafruit, website: https://learn.adafruit.com/pir-passive-infrared-proximity-motion-sensor/how-pirs-work.
[14] Shankar, M., et al, "Human-tracking systems using pyroelectric infrared detectors," Optical Engineering, 45 (10), (2006).
[15] Wahl, F., Milenkovic, M., Amft, O., "A distributed PIR based approach for estimating people count in office environments," presented at the 2012 IEEE 15th International Computational Science and Engineering, Washington DC, US, (December 2012).