Room Occupancy Estimation Through WiFi, UWB, and Light Sensors Mounted on Doorways Hessam Mohammadmoradi, Shengrong Yin, Omprakash Gnawali University of Houston International Conference on Smart Digital Environment (ICSDE ’17) July 21, 2017

Motivation Huge Energy Lost through Buildings 40% of Total Energy Consumed in the US [1] Efficient Management of HVAC 25% Reduction in Energy Waste [2] [1] Buildings Energy Data Book. 2011. Energy Efficiency and Renewable Energy. US department of energy (2011) [2] Alex Beltran, Varick L Erickson, and Alberto E Cerpa. 2013. Thermosense: Occupancy thermal based sensing for hvac control. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings. ACM, 1–8.

Our Goal Counting people is a key enabler for HVAC control in a smart building Investigate feasibility of using Wireless Sensing to build a people counting solution which is : Inexpensive Reliable Accurate Easy to Mount Privacy Preserving

State of the Art People Counting Solutions Application Cost ($) Privacy Preserving Level Scalability Training Required Mounting Flexibility Break Beam Counting <= 10 High Yes No PIR Presence Ultrasonic <= 100 Moderate RGB Camera Low IR Imager <= 25 Our Solution

Human presence impacts link Approach Count the number of entrances and exits Update occupancy count Put sender and receiver on sides of the door Create an active link between them Human presence impacts link

Channel State Information Channel State Information (CSI) is channel properties of communication link Summarizes impact of environment on signal propagation Scattering Fading Power decay with distance The received signal is compensated using CSI information to build reliable communication link

Utilizing WiFi to Count People IEEE 802.11n defines 64 sub-carriers for 20 MHz bandwidth 128 sub-carriers for 40 MHz bandwidth Sub-carriers face different levels of fading in presence of humans Sub-carrier characteristics can be monitored using CSI information Amplitude and phase information per sub-carrier

Fading levels among WiFi subcarriers in presence of humans Noone between sender and receiver Two persons are standing between sender and receiver Different Subcarriers face different levels of fading in presence of humans

Multiple People between Sender and Receiver One Person standing between sender and receiver Two Persons standing between sender and receiver SNR fluctuation range has a direct relationship with the number of people who are standing in the doorway

Counting People Utilizing WiFi Signals Procedure Count number of people who are walking through the door cnt ← 0 //Number of People cntmax ← 0 // Maximum Number of People CSI ← Channel State Information For sc in CSI sub-carriers do snr ← SNR value for sc snravg ← Average SNR seen for this sub-carrier If |snr − snravg | > α ∗ |snravag | then cnt ← 3 Else if |snr − snravg | > β ∗ |snravg| then cnt ← 2 Else if |snr − snravg| > γ ∗ |snravg| then cnt ← 1 if cntmax < cnt then cntmax ← cnt Return cntmax α = 80% Β = 50% γ = 25%

Utilizing UWB Signals to Count People UltraWideBand wireless signal Bandwidth > 0.2*(Central Frequency) UWB Signal’s Unique Features Resilient to multipath reflection Accurate indoor positioning systems (<50 cm error) Short range, high data rate applications such as room level video streaming Human presence changes the characteristics of the communication channel between sender and receiver

Channel Impulse Response Channel Impulse Response (CIR) is representative of channel characteristics Amplitude and phase information for received signal across time domain (sampling every 1 ns) First Path Signal: The first CIR sample with power significantly higher than background noise

UWB Channel Characteristics in Presence of Humans CIR in presence of two persons vs no person

Counting People Utilizing UWB Signals Procedure Count number of people who are walking though door fppower ← First Path Signal Power noise ← Background Noise cnt ← 0 //Number of People If fppower− noise < α ∗ fppower then cnt ← 0 Else if fppower − noise < β ∗ fppower then cnt ← 1 Else if fppower− noise < γ ∗ fppower then cnt ← 2 Else if fppower − noise < φ ∗ fppower then cnt ← 3 return cnt α = 75% Β = 60% γ = 30% φ = 20% We are interested in instant changes of first path signal compared to background noise

Ambient light level with or without human walking through the door. Illuminance level without presence of human Illuminance level with presence of human

Utilizing Ambient Light Sensing to Count People The key idea is monitoring ambient light’s luminance level and use changes in the level of light due to human presence as an indicator for presence of people Just one sensor is enough to detect presence of people Illuminance level collected when subjects are walking through the door 18 walks through the door

Light Sensing based People Counting Procedure: Detect Presence of People lumavg ← Average Luminance Level in Empty Background lum ← Measured Luminance Level (Window Average) presence ← False // Is Someone There? if lum − lumavg > α ∗ lumavg then presence ← True else presence ← False return presence α = 60%

Evaluation – Experiment Setting TREK1000 Evaluation Kit - UWB Intel 5300 NIC – WiFi Chip TelsoB – Light Sensor WiFi Experiment Setup

Controlled Experiment – Height Flexibility Sender and Receiver nodes mounted at different heights Ten people asked to walk through the door Changes in CSI in different heights (WiFi) Changes in CIR in different heights (UWB) The proposed system can detect the presence of people at deployment heights.

Controlled Experiment – Multiple People Sender and Receiver nodes mounted at the height of 120 cm 1, 2, 3 and 4 people are asked to stand between sender and receiver Changes in CSI with different number of people standing at the Doorway Changes in CIR with different number of people standing at the Doorway Up to 2 people standing side by side are detected by the system

Controlled Experiments – Door Width Door Size 90 cm , 160 cm Deployment Height 120 cm 10 people Accuracy of Counting People inside the Room in a Door with 90 cm Width Event Ground Truth WiFi (%) UWB(%) Light(%) Entrance 55 53 (96%) 54 (98%) 52 (95%) Exit 52 (95%)) 55 (100%) Accuracy of Counting People inside the Room in a Door with 160 cm Width Event Ground Truth WiFi (%) UWB(%) Light(%) Entrance 60 56 (93%) 58 (96%) 54 (86%) Exit 55 (91%)) 59 (98%)

Uncontrolled Experiments Door Size (Conference Room) 100 cm Deployment Height 120 cm Ground Truth Manually counted Accuracy of Counting People inside the Room in Uncontrolled Environment

Uncontrolled Experiment – Combined Solution Door Size (Conference Room) 100 cm Deployment Height 120 cm Ground Truth is counted Manually Combined Solution Majority vote was taken to estimate occupancy inside the room Performance Evaluation in Uncontrolled Environment - Combined Solution Event Ground Truth Combined Solution Entrance 73 70 (96%) Exit 70 68 (97%)

Discussion Light sensing technology is very sensitive to ambient light Rooms with direct sun light are not suitable for light based people counting Maximum detectable crowd size Up to 3 person walking side by side Build more accurate model using machine learning ! All the investigated solutions are very inexpensive Light based people counting solution costs less than 10$

Conclusion Low cost wireless sensing technologies (WiFi, Ultra-wideband and visible light), and others, can be used for people counting The human presence impacts wireless channel Combination of sensing technologies can achieve 96% accuracy in estimating the number of people inside the room Omprakash Gnawali gnawali@cs.uh.edu http://www2.cs.uh.edu/~gnawali/