Radio Sensing: A Monitoring Technology for Resilient Places

Dr. Neal Patwari 26 April 2017

Research area: Radio Sensing

The wireless is the sensor

“The number of bars” “Signal strength”

How it works: Location from radio sensing

1. Plug in sensors and place them on a map

2. They form a mesh network covering area

3. Measure changes on each link line

4. Motion is detected and shown on the map

Accurate to about 1 meter Can be hidden See through objects Not video or audio

surveillance Real-time access Easy deployment Low cost ($500 / 1500 ft2)

Product: Home motion location sensor

http://xandem.com

Track wakeup time Translate tracking to activity class (meal prep, bathroom) Estimate speed of walking (look for changes over months) Collect data simultaneously from other sensors Detect and track multiple people Distinguish pet via radio tag on collar

In the lab: Extensions for health monitoring

O. Kaltiokallio, R. Jäntti, and N. Patwari, ARTI: An adaptive radio tomographic imaging system, IEEE Trans. Vehicular Technology, Jan 2017. O. Kaltiokallio, M. Bocca, and N. Patwari, Follow @grandma: long-term device-free localization for residential monitoring, IEEE SenseApp, Oct. 2012.

Two sensors Body inhalation & pulse changes multipath waves → Signal strength (sum of waves) changes

Radio waves

Setup: In-home non-contact RR / HR monitor

N. Patwari, L. Brewer, Q. Tate, O. Kaltiokallio, and M. Bocca, Breathfinding: A Wireless Network that Monitors and Locates Breathing in a Home, IEEE J. Selected Topics in Signal Processing, Nov. 2013

Small variations from breathing are monitored

Anh Luong, Alemayehu Solomon Abrar, Thomas Schmid, and Neal Patwari, RSSI step size: 1 dB is not enough!, in Proc. 3rd ACM Workshop on Hot Topics in Wireless, 3 Oct. 2016.

New generation hardware provides increased accuracy

1.9 in Sig

nal

Stre

ngth

(dB

) R

IP B

elt S

igna

l (A

DC

Val

ue)

Time (seconds)

Peaks, valleys of signal strength line up with those measured with a respiratory impedance plethysmo-graphy belt

Breathing change in RSS is small (vs. motion) Average errors of about 1 breath/min (vs. RIP)

Smaller changes from pulse can be monitored

By filtering noise and slow drift, we can see breathing clearly.

Filtering out breathing we see a signal at harmonics of the pulse rate, 10x smaller than breathing

Al. Abrar, et al. Save Our Spectrum: Device-free Human Sensing Using Single Carrier Radio, (submitted) ACM SenSys 2017

Sig

nal

Stre

ngth

(dB

)

Time (seconds) Time (seconds)

Beats Per Second Breaths Per Second S

igna

l S

treng

th (d

B)

Pow

er S

pect

ral

Den

sity

Pow

er S

pect

ral

Den

sity

Pulse estimation accuracy: Experimental Ground truth: Pulse ox Average Error:

1.6 bpm lying 3.7 bpm sitting

Pul

se R

ate

(bre

aths

per

min

ute)

Time (seconds)

User 1 User 2

Al. Abrar, et al. Save Our Spectrum: Device-free Human Sensing Using Single Carrier Radio, (submitted) ACM SenSys 2017

Conclusion

Goals: Find research collaborators / partners Test next-gen system Extend features for home health Enable research Enable consumer systems

We can readily monitor a resident’s: location, breathing rate, pulse rate, without their active participation

Extra Slides

PRISMS In-Home Sensing System Architecture Visualization

Tool

Sensors / Actuators

Gateway

Server

Home Assistant

Local DB e.g.

Influx

BLE WiFi Zwave

Cellular / Home Broadband

Gateway

Raspberry Pi

Phillip Lundrigan, Kyeong T. Min, Jimmy Moore, Sneha Kumar Kasera, Kathy Sward, and Neal Patwari “EpiFi: An In-Home Sensor Network Architecture for Epidemiological Studies”, (submitted) SenSys 2017.

Deployment A: Gateway Internal Data

Location tracking using

smartphones

Hue light bulbs

Z-Wave multi-sensor

Wifi Switch and motion detector, Z-Wave door sensor

WiFi temperature and humidity sensor

Z-Wave energy meter

Utah modified Dylos sensor