Radio Tomographic Imaging for Fall DetectionCameron Fleming, Daniel Lazar, Christine Weston, Jichuan Li, Dr. Robert Morley, Dr. Arye Nehorai

Department of Electrical and Systems Engineering

Abstract

Introduction

Implementation

Algorithm

References and Acknowledgments

Results

We use 10 NI USRP-2920 units with full duplex daughterboards

to simultaneously transmit and receive. We chose a carrier

frequency of 1.4GHz, which attenuates well in a person’s body.

Each unit transmits at a different baseband frequency with 5kHz

spacing in between each signal. This prevents overlap between

the transmitted frequencies, as the USRPs do not have

synchronized internal oscillators.

Thirty percent of people over the age of 65 fall each year; often

they are alone in their homes [2]. In the event of a fall, 80% of

people over the age of 90 with wearable help button devices do

not push the button, frequently because they are not wearing the

device. Device-free fall detection systems, such as those based

on radio tomographic imaging (RTI), alleviate this concern.

Since the human body attenuates many of the radio frequency

(RF) waves passing through it, RTI uses measurements of the

relative power of received RF waves to locate a person. 3D RTI-

based fall detection detects the state of the person’s position

(lying, sitting, or standing) and decides whether the transition

between states occurred quickly enough to likely be caused by a

fall.

Mid-position

[2] C. Todd and D. Skelton, “What are the main risk factors for falls among older people and what are the most effective interventions to prevent these falls?” WHO Regional Office for Europe, Tech. Rep., 2004.

[3] Y. Zhao, N. Patwari, J. M. Phillips, and S. Venkatasubramanian. Radio tomographic imaging and tracking of stationary and moving people via kernel distance. ACM ISPN, 2013

[4] Patwari, Neal, and Piyush Agrawal. "Effects of correlated shadowing: Connectivity, localization, and RF tomography." ACM IPSN, 2008. International Conference on 22 Apr. 2008: 82-93.

[5] Kaltiokallio, Ossi, Hüseyin Yiğitler, and Riku Jäntti. "A Three-State Received Signal Strength Model for Device-free Localization." arXiv:1402.7019 (2014).

[6] Wang, Zhenghuan et al. "Multichannel RSS-based Device-Free Localization with Wireless Sensor Network." arXiv:1403.1170 (2014).

Thank you Jichuan Li, Ed Richter, Dr. Robert Morley, and Dr. Arye Nehorai for all the indispensable assistance and guidance provided.

We locate people by implementing kernel-based radio

tomographic imaging [3]. This involves obtaining the received

signal strength values (RSS) for each transmitter/receiver pair.

These values are used to construct a short-term histogram,

which reflects recently acquired values, and a long-term

histogram, which acts as a baseline RSS distribution. The

magnitude of the kernel distance between these histograms

determines the attenuation on the link, which is used to

reconstruct the attenuation in each pixel. The location of a person

is estimated as the pixel with the maximum attenuation.

Fall-related injuries pose a serious threat to the health and

autonomy of older adults. As wearable fall detection systems are

often not adequate in reporting falls, we implement a 2D radio

tomographic imaging system for use in environmental fall

detection.

This system can be used in conjunction with a track-before-

detect approach to determine if a person has fallen. Alternatively,

the system can be extended to image 3D areas and detect falls

with a hidden Markov model [1]. In our implementation, we

introduce a simultaneous transmit and receive method that

allows a very fast frame rate, facilitating the implementation of a

2D fall detection system.

There are many possible extensions of this 2D RTI system. A

track-before-detect system based on particle filtering will enable

tracking and basic fall detection. A second layer of USRPs at ankle

height can be added for 3D RTI and a more accurate fall detection

using a 3-state hidden Markov model. There is room to investigate

extending the sensitivity region and accuracy of the links [5] and

controlling for multipath interference [6].

Visual output of imaging algorithm

Further Work

20kHz 25kHz 30kHz 35kHz

40kHz

45kHz50kHz60kHz

65kHz

55kHz

Object in line of sight Pixels in line of sight of two transceivers

We effectively detected a person’s location with our

implementation of kernel-based radio tomographic imaging and

achieved a frame rate of over 20/sec. We created a visualization

that weights each pixel’s color according to the value of its pixel

attenuation estimator [4]. The pixel whose estimator has the

highest attenuation corresponds to the location of the person.

Baseband power spectrum of a three unit system

No object in line of sight

Visual output of imaging algorithm

Test of 2D RTI system

Standing Lying

Hidden Markov model states for 3D RTI

Algorithm

[1] B. Mager, N. Patwari and M. Bocca, “Fall Detection Using RF Sensor Networks”, IEEE PIMRC, 2013