A Prototype Sensor Node for Footstep Detection

Giorgos P. Mazarakis, John N. Avaritsiotis Department o f Electrical and Computer Engineering National Technical University of Athens (NTUA)

Athens, Greece gemazar@mail.ntua.gr , abari@cs.ntua.gr

Abstract-Persons moving over ground can be detected from vibrations induced to soil in the form of seismic waves which are measured by geophones or expensive MEMS accelerometers. We are proposing a sensor node that uses a low-cost bending mode piezoelectric accelerometer, operating near its resonant frequency, specially designed for footstep detection.

I. INTRODUCTlON

In the last years the advances in sensors and actuators, microelectronics and wireless networking, has given a significant boost in the development of sensor networks that track real life phenomcna [l]. A lot of research is being made across the world on wireless sensor network applications for security, surveillance and force protection. Seismic and/or acoustic detection and tracking of personnel and vehicles is of major importance in such applications [2,3].

Unlike other systems, sensor networks have certain limitations on resources, such as power, computational and networking capabilities that have to be calculated

'upon design. Furthermore, cost must be kept minimal when designing an application where large number of nodes is needed, such as boarder security or large area monitoring. For example the use of a more expensive DSP instead of a microcontroller will result in advanced computational capability white having a drawback in energy consumption and overall system cost.

l h e same rule applies to the sensing dements of the node [1,4,5]. Seismic detection is being made using commerciat geophones, piezoelectric accelerometers and in earlier projects for Unattended Ground Sensors (UGS) low-noise, micro-machined servo accelerometers (MEMS) with performance comparable to geophones [6- 111. These sensors provide good results in seismic detection having the drawback of significant cost and size.

We have developed a node that uses a prototype low- cost bending mode, piezoelectric accelerometer to detect a human that walks within range and is configured to operate near its resonant frequency for enhanced sensitivity. The system detects impacts induced by a

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walking person, makes a decision for human presence based on the frequency of the signal and the cadence of the impacts. I t utilizes a built in radio transceiver to forward the result to a header node of the wireless sensor network.

11. FOOTSTEP CHARACTERISTICS

Human activity generates vibrations that propagate away from the source as seismic waves. Most energy is distributed in Rayleigh waves (70 %), white diminishing in amplitude as R-', and the remaining amount (30 %) is transmitted by body waves (shear and compressional), while diminishing in amplitude as K2 [10,12]. The signal has also frequency dependent attenuation characteristics ~ 3 1 .

A person walking on the ground produces a number of impulses, as the foot hits the ground, which propagate through soil as "seismic" waves. Footstep signature characteristics are outlined below.

A . Inrpuhive Nature A human's walk may be modeled as a series of

vertical impacts induced to soil by the foot of the walker. These impacts are of short duration and occur on intervals grater than the time needed for them to attenuate [IO]. They result on the excitation of the ground at its natural frequencies and travel away from the source on the elastic half-space of the ground (Figure 1). Most of the energy of such signal is distributed in the band of 10 to 100 Hz [12].

3 I - . - ______- __c ~-

B. Cadence The rate that these impulses occur depends on the

walker (typically 2 Hz, varying fiom 0.9-1 Hz for slow, cautious walking to 3.5 Hz for fast running) and it is quite repetitive, providing a characteristic seismic signature easily recognized in time domain series (Figure 2). This series of "spikes" can be distinguished from other noise such as moving vehicles, wind blows or non repetitive impulses. In this way the cadence of the signal can be used to detect human presence in a simple, power-aware, sensor node ( 1 1,121.

I I ,

L .__-_I____ 2--" I -_-I I ' nC 1 81 1 l l d J. %

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Fig. 2. Series of fuutsteps with distinct, repetitive pattern

111. PIEZOELECTRIC ACCELEROMETER

A . Basics A walking person produces ground acceleration of

10" to to 10" g 1141. The use of most low-cost commercial accelerometers installed in some motes is inappropriate for footstep detection due to their poor resolution and sensitivity. The fundamental noise limit of the accelerometer, which determines the smallest acceleration that the sensor can detect, is also an issue that must be dealt with when choosing the appropriate sensor [IS].

In piezoelectric accelerometers the primary sensing element is a piezoelectric element manufactured in a way to produce an electrical signal when stressed by vibratory forces. The three basic structural designs that are used in accelerometers are corn pression, shear and flexuraI or bending designs. Bending mode designs have lower resonant frequencies and very high output (up to 100 V/g) and are mostly used in low frequency seismic applications [ 141.

g while a moving vehicle

B. Footstep detection sensor setup Seismic sensors and accelerometers operate in their

linear region, away from resonant frequencies. Geophones operate in frequencies higher than the resonant, typically below 20 Hz, while piezoelectric or MEMS accelerometers operate below their resonant

frequency (which is typically very high) and in some cases they reach DC (MEMS) [6 ] .

Sensors that operate away from th,eir resonant frequencies, in their linear region, provide accurate measurements but loose in sensitivity that can be achieved near resonance. We propose the use of a prototype bending mode piezoelectric accelerometer for a footstep detection node. The sensor -operates near resonance since sensitivity is much more impoitant than linearity in such an application. In this way, it is used as an electromechanical pass-band filter than a vibration sensor 14,151.

C. Sensor Design

following characteristics: The piezoelectric sensor was designed with the

o Low power, simple design and signal conditioning

High sensitivity and increased resolution

Low noise

e Low cost, small and PCB mount

I t consists of a cantilever piezoelectric bimorph beam with a concentrated seismic mass on the free edge and the other edge is soldered on the PCB. The frequency response of the sensor is shown in figure 3. The resonance is measured at 380 Hz and the corresponding acceleration noise spectral density is equal to 250ng/&at 1 Hz [15]. A charge amplifier is used for the signal conditioning, built from two TLV2772 low- noise and low-power op-amps.

10"

! I d ' ,U' d t9

10' (t'

Fnqwrr Blr)

Fig. 3 . System frequency respome, (a) cont. line: Simulated response (b) da5hed line: Measured response

IV. NODE DESIGN

A. System Architecture Since energy i s one of the most valuable resources on

a wireless sensor node, special care should be taken to

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design a power-aware node .Our approach is to make'the footstep detection algorithm as lightweight as possible in order to run efficiently on a system with limited computation capabilities like those provided by a microcontroller. The prototype node contains a microcontroller with a built in 10 bit A D converter (ATMega32), the seismic sensor with its signal conditioning circuitry and a wireless modem for communications with other nodes and the head node (portable computer) (Figure 4).

Seismic

Microcontroller Atmel Mega32

Power

Fig 4. Footstep Detection Node Architecture

B. Footstep detection algorithm The footstep detection algorithm was designed to

require minimum computations and minimum memory resources. Furthermore, it was implemented using integer arithmetic and special care for limiting energy consumption was taken. The basic concepts of the algorithm are:

Calculate the baseline of the signal and determine adaptive threshold levels above noise that follow temperature drifts of the output.

Sample the Sensor at a slow rate and enters sleep statc when there arc no significant vibrations. When threshold is exceeded increase sampling rate for correct frequency measurement.

Calculate the frequency of the signal. Ifit matches the resonant frequency of the beam there is a strong indication that the cause of the disturbance is an impact.

Calculate pattem repeatability, which is the cadence of footsteps. If three OF more "spikes" are found and the sensor is excited at it's resonant frequency, the system sends its decision for footstep detection to the header node.

V. RESULTS

A. Simirlation The algorithm waz, first simulated and evaluated on a

PC using MATLAB using data sets of measured data taken from the prototype sensors. Two versions of the algorithm were tested.

The first is using an adaptive threshold that is calculated according to the noise of the signal using the folfowing formula:

If S ( n ) is the signal and q ( n ) is the threshold, then

if S(n)27;(n) then the threshold remains the same:

q( n ) = q ( n - I ) ,

if S(n)<q(n) then the threshold is updated according to:

~ l ~ ( . ) = ( l - u > ~ j i ( . - l ) . u ~ ~ ( n )

$( n)=( I-a)q? (n-I)+a[ si (n)-p&I)] 2

( t ~ ) = / t i (n-I)+Caj ( t ~ )

Where pi (n) and ai(.) are the mean value and standard deviation of the signal accordingly. Coefficient C is used to keep a constant low false alarm rate and a is a factor between 0 and 1 (Figure 5) [16].

~. - - - - __ ..._............I .... t i + .... ....-..,, .... .........

I" ..................... i ..... .".,l ._,. . ..... tim.illlll.i .. ; 1 ''___

. . . . . . . . . . . . i .... 1- ....................... ._ i

a 25 sec data set from a person valking in circles

The second is a lighter version of the previous algorithm using fixed -but reconfigurable thresholds calculated from the mean value of the signal (Figure 6) .

417

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m .“.I ........ _I.. .... 1. ”.l.l - ............ .... ..... ... ..-............ . 1 . ._,.... j

t - 3” .-.-A Fig. 6. Fixed threshold for footstep detection algorithm calcukired on a

25 sec data set from a person walking in circles

The basic features of the algorithm are:

Calculate an initial baseline when there is no vibration and follows the drift of the signal due to temperature changes.

Apply fixed but reconfigurable thresholds to the calculated baseline.

Calculate the frequency of the vibration and the cadence of the footsteps. If the beam is vibrating at its resonant frequency and the cadence of the impulses is within the aforementioned limits the system is informed that a walking person is detected.

The two algorithms were tested using the same 25 second data set. For baseline calculation the adaptive threshold algorithm required about 380.000 operations (including a square root calculation) while the fixed threshold algorithm required about 30.000. The latter was finally implemented on the node to conserve power but the results of the simulation with the adaptive algorithm were better as it follows the noise of the signal and calculates a value for the threshold above it.

.

B. Field measurenrenfs Two prototype nodes have been constructed to test the

performance of the system in real environment. A small test-bed of two nodes that were wirelessly connected to a header node was set on grassy terrain. A person was asked to walk normally in circles of 6m and Ipm of diameter and then to walk in a straight line starting SKI from the nodes, passing I m from the nodes and continue Sm from the other side. Measurements were repeated with the person -running instead of watking. The results were encouraging and the small network could detect the walking/running man in most occasions and send the decision to the header node.

VI. CONCLUSIONS AND FUTURE WORK The resuIts from testing the system in real

environment showed that footstep detection can be achieved using small and low-cost bending type piezoelectric accelerometers and simple, energy-aware algorithms running on a microcontroller. More work is required in order to increase the range and sensitivity of the sensor by lowering its resonant frequency and to optimize the algorithms of the system for lower false alarm rate. Frequency calculation using methods that require little computations such as wavelets and extracting bearing information from the network are also under consideration.

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