PIR OCCUPANCY ESTIMATION SYSTEM

1 . A PIR occupancy estimation system, configured to estimate a number of occupants in a target space, and comprising:

a PIR motion sensor with a field of view of an area where occupants are expected to be present, and adapted to output a PIR signal; and

a control apparatus adapted to receive the PIR signal to compile an aggregate of the minor and major motion behaviors of the occupants in the field of view and generate an occupancy estimation count accordingly.

2 . The PIR occupancy estimation system according to claim 1 , wherein the control apparatus is adapted to operate an algorithm to process the PIR signal from the PIR motion sensor.

3 . The PIR occupancy estimation system according to claim 2 , wherein the algorithm is adapted to utilize a bandpass filter to filter the PIR signal to remove unwanted bias and high frequency noise.

4 . The PIR occupancy estimation system according to claim 2 , wherein the algorithm is adapted to break the PIR signal into a plurality of frames.

5 . The PIR occupancy estimation system according to claim 4 , wherein the plurality of frames are generated with a 50% overlap.

6 . The PIR occupancy estimation system according to claim 4 , wherein the algorithm is adapted to perform a transformation, comprising a time-frequency transformation or a feature extracting transformation, on the plurality of frames.

7 . The PIR occupancy estimation system according to claim 6 , wherein according to the plurality of frames and an output of the transformation for each of the plurality of frames, the algorithm is adapted to extract statistical features from each of the plurality of frames and concatenate the statistical features into a feature vector.

8 . The PIR occupancy estimation system according to claim 7 , wherein the statistical features includes at least one of a Laplace spread parameter, a range between the smallest and largest samples in the frame, a zero crossing rate, a mean crossing rate, an entropy, a motion count, percent positive values, percentiles, and a slope.

9 . The PIR occupancy estimation system according to claim 7 , wherein the algorithm is adapted to distinguish and characterize the plurality of frames between the major and the minor motion behaviors, and extracts the frame where the minor motion behaviors detected.

10 . The PIR occupancy estimation system according to claim 7 , wherein according to the feature vector, the algorithm is adapted to utilize a machine learning model to estimate the number of occupants in the target space and generate an approximate estimation.

11 . The PIR occupancy estimation system according to claim 10 , wherein the algorithm is adapted to process the approximate estimations from the machine learning model through a stability and smoothing phase to generate smoothed estimations, and generate the occupancy estimation count through binning the smoothed estimations.

12 . The PIR occupancy estimation system according to claim 11 , wherein the algorithm is adapted to apply a supervisory logic to the approximate or smoothed estimations to eliminate anomalies and edge cases.

13 . The PIR occupancy estimation system according to claim 1 , further comprising a cloud server adapted to store data, wherein the control apparatus comprises a transceiver in communication with the cloud server.

14 . The PIR occupancy estimation system according to claim 13 , wherein the PIR motion sensor and the control apparatus form an occupancy estimation unit, and the PIR occupancy estimation system comprises a plurality of the occupancy estimation units which are all in communication with the cloud server.

15 . The PIR occupancy estimation system according to claim 1 , further comprising a plurality of additional sensors, comprising at least one of an audio sensor, a CO2 sensor, a Bluetooth device sensor, an infrared temperature sensor, an area temperature sensor, a humidity sensor, a light level sensor, and a light color sensor, working in conjunction with the PIR motion sensor.