On-Site Server

Sensor Hub 1

Wireless Router

Sensor Hub 2

Sensor Hub 3

Sensor Hub 4

Sensor Hub 5

VM 1 VM 5VM 4VM 3VM 2

(a)

(b)

(c)

Figure 1. (a) System architecture, hardware components, and network connections of the HPDmobile data acquisition system.(b) Custom designed printed circuit board with sensors attached. Sensors, clockwise from top right, are: camera, microphone,light, temperature/humidity, gas (CO2 and TVOC), and distance. (c) Final sensor hub (attached to an external battery), asinstalled in the homes.

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HPDmobile Data Acquisition System & Database

Captured Data

Sensor Selection

We captured data from six residences to learn about factors affecting occupancy predictions.

We created a publicly available data set that other researchers could utilize for occupancy prediction tasks.

ID Occupants Pets Type Home Size (m2)

% Area Monitored

Hubs Deployed Days % Occ

H1 4 adults 1 cat House 149 32 5 30 0.82H2 1 adult None Apartment 72 53 4 17 0.61H3 4 adults 1 dog House 285 28 5 35 0.78H4 3 adults None Apartment 93 36 5 17 0.82H5 2 adults 1 cat Apartment 54 48 5 15 0.76H6 1 adult None Apartment 62 58 4 38 0.47

Measurement Units or Description Sampling Frequency Precision

Temperature Degree Celsius (°C) 0.1 Hz ± 0.1 °CRelative Humidity Percentage (% rH) 0.1 Hz ± 0.1 % rHCarbon Dioxide (eCO2) Parts per million (ppm) 0.1 Hz ± 1-31 ppmTotal Volatile Organic Compounds (TVOC) Parts per billion (ppb) 0.1 Hz ± 1-32 ppbAmbient Illuminance Lux (lx) 0.1 Hz unspecifiedGround truth occupancy Binary: occupied/vacant 5 minute N/AAudio (as collected) 10-second WAV file 8 kHz 18 bitImages (as collected) 112k pixel b\w PNG file 1 Hz 36 dB S/N ratioAudio (as available) Audio signal as CSV file 80 Hz N/AImages (as available) 1,024 pixel b&w PNG file 1 Hz N/A

Table 1: Home and occupant attributes. Days refers to number of days of data that were released from home, while % Occ refers to percentage of time home was occupied.

Table 2: Summary of all modalities as collected by data acquisition system and as available for download. Precision is as specified by sensor product sheets.

Figure 1 (above right): Home diagram with sensor locations marked.Figure 2 (below right): Hardware components and network connections.Figure 3 (above left): Sensor hub with sensors with battery.

BEDROOM

KITCHEN BATHROOM

LIVING ROOM

PATIO

8.1

m

6.4 m

N

RS1RS2

RS3

RS5

RS4H6-red

Figures 4: Data collected in a 24 hour period. Clockwise from top left: illuminance, TVOC, occupancy, CO2, relative humidity, and temperature.

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1Home

H1H2H3H5H6

Image to Audio

False Positive Rate

True

Pos

itive

Rat

e

To identify the most important sensing modalities and locations, we performed a full factorial analysis comparing predictive results of our sensor fusion algorithm for all combinations of hubs and modalities, with the environmental modalities considered jointly.

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

H1 H2 H3 H5 H6

None to Image Audio to ImageNone to Audio

Figure 6: Audio waveforms before (left) and after (right) multistep processing procedure for obfuscation.

Figure 5: Images as collected (left) and after downsizing to 32x32 pixels (right).

Figure 8: Changes in classification performance when the kitchen hub is switched form nothing to audio, nothing to image, and audio to image.

Figure 7 (right): Results of factorial analysis comparing audio and image collection. Each point shows one permutation. Above shows one result.

Testing and System Integration

Contact: Gregor Henze, University of Colorado Boulder, gregor.henze@colorado.edu

Battery-Free RFID Sensor Network with Spatiotemporal Pattern Network Based Data Fusion System for Human Presence Sensing

Investigators: Gregor Henze (PI, UCB), Anthony Florita (NREL), Soumik Sarkar (ISU), Joshua Smith (UW) ❦ Research Assistants: Maggie Jacoby (UCB), Jasmine Garland (UCB), Sin Yong Tan (ISU), Ali Saffari (UW), Mohamad Katanbaf (UW)

Date: May 2021 Project Status: Month 36 of 36