BIOSIGNALS WITH A FLOOR SENSOR

Near Field Imaging Floor Sensor Measures Impedance Changes in the Torso

Henry Rimminen and Raimo Sepponen

Department of Electronics, Helsinki University of Technology, Otakaari 7B, Espoo, Finland

Keywords: Remote sensing, Non-contact measurement, Cardiac monitoring.

Abstract: We analyse biosignals recorded with a near field imaging floor sensor, using a test group of five people.

This human tracking system is capable of non-contact biosignal recording. A time domain integration

method is used to extract periodic cardiac waveforms from the raw signals, while an ECG signal is used as a

trigger for windowing. The most favourable posture for cardiac monitoring is when the test subjects are

lying prone on the sensor floor. A clear correlation between the test subjects can be found when waveforms

in the lying prone or supine postures are compared. The respiration monitoring capability is also discussed.

1 INTRODUCTION

In this study, we analyse the recorded biosignals of a

near field imaging floor sensor (Rimminen, 2008),

using a test group of five people. The main purpose

of this floor sensor is to track people walking on top

of it, but the proposed applications of the floor

sensor, such as care for the elderly and seclusion

monitoring, would most probably benefit from a

vital signs monitoring capability.

Recently, some promising results have been

presented regarding the remote sensing of the human

body. Using electric potential probes with input

impedances up to 10

Ω, a clear cardiac signal has

been recorded from a distance of 3 millimetres

(Prance et al., 2000) and later from a distance of one

metre (Harland, 2001). Low impedance charge

amplifiers have also proved their strength in off-

body sensing. This technique has produced good

results from a recording distance of up to 10

millimetres (Smith, 2004). These methods measure

biopotentials produced by the human body, and no

electrical stimulus is generated by the measurement

system.

Some promising experiments have also been

made with electret films, which produce a charge

under pressure. These films produce clear cardiac

and respiratory signals when applied to the chest and

to the chair on which the subject is sitting

(Alametsä, 2004). This method measures solely the

fine movements of the body.

Unlike the works discussed above, our method

uses a 90-kHz electrical stimulus to measure

impedance changes in the torso, with no galvanic

contact with the body. This kind of measurment is

often referred as Electric field tomograpy

(Korjenevsky, 2004), and has some applications

using planar electrode arrays (Tuykin, 2007) similar

to us. Instead of a spatial analysis, we sample the

signal from one electrode, and analyse the results in

the time domain. As far as we know, there is no

implementation of a biosignal monitor integrated in

a non-pressure-sensitive floor sensor.

The goal of this study is to analyse cardiac

activity in the biosignals recorded with the near field

imaging floor sensor. We also aim to find out which

the most favourable postures are for this kind of

recording. A secondary goal is to observe respiration

in the recorded signals.

2 MATERIALS AND METHODS

2.1 Measurement System Overview

The positioning system under study measures

impedance changes between conductive elements in

a thick film sensor matrix (Rimminen, 2008). The

rectangular sensor elements have a pitch of

approximately 50 cm x 25 cm. One measurement

unit can cover up to 255 sensor elements and 32 m

125

Rimminen H. and Sepponen R. (2009).

BIOSIGNALS WITH A FLOOR SENSOR - Near Field Imaging Floor Sensor Measures Impedance Changes in the Torso.

In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 125-130

DOI: 10.5220/0001120901250130

 SciTePress

f = 90 kHz

MUX

G = 70 dB2 - 100 Hz

ADC

Tuned transformer

Sensor matrix

under floor surface

Biosignal

channel

ADC

DC-

channel

2.5 kHz

DAC

G = 40 dB

Bias

removal

PSD

Figure 1: Block diagram of the measurement system. The

local oscillator (LO) signal is fed to one sensor element at

a time using an array of multiplexers.

of floor area. This means that small and medium size

rooms can be covered entirely. These sensor films

are installed under common dielectric floor

coverings with thicknesses of up to 10 mm. The

plastic floor covering in our test room is 3

millimetres thick. This justifies the use of the term

“non-contact measurement”.

The amplitude of the recorded biosignal is

assumed to be proportional to impedance changes in

the body. Because of this assumption, we refer to the

recorded samples as ΔZ signals.

The ΔZ recording is performed by feeding

alternating current to a single sensor element and

grounding the others (see Figure 1). The amplitude

of the current is measured using a tuned transformer

and phase sensitive detector (PSD). These structures

perform well in rejecting common mode EMI. After

this, the signal is fed through a band-pass filter and

amplified by 70 decibels. Then we use a 10-bit a/d

converter integrated in a microcontroller. The band-

pass filter has a 50-Hz twin “T” notch to reject

interference from the mains (National

Semiconductor, 1969) (see Figure 2). The DC

channel in Figure 1 is used for human tracking and

is not discussed in this study.

-2

-1

Frequency [Hz]

Gain [dB]

Figure 2: The simulated frequency response of the

Biosignal channel.

Figure 3: a) Sternal height recording b) Abdominal height

recording. The grey rectangles represent the floor sensor

elements.

2.2 Test Arrangement

We recorded 20 second samples of five different test

subjects in four different lying postures. The

postures were the following: prone, supine, left

lateral, and right lateral. Two recordings were taken

in each posture: from sternal height and from

abdominal height (see Figure 3). We selected a fixed

sensor element for the recording, which was marked

on the floor covering. The total number of 20-second

samples was 40. The test subjects were breathing

normally during the recordings.

An ECG signal between electrode locations V2

and V4 was recorded simultaneously during every

floor sensor recording. The ECG signal was

acquired using wet electrodes and an

instrumentation amplifier with an adjustable band-

pass filter (PRE AMP Model 5113, Princeton

Applied Research, Tennessee, USA).

The test group consisted of five M.Sc. students;

three males and two females. The average weight

was 74 kilograms, and the average age was 23 years.

The use of elderly people as test subjects was not

feasible because of the difficult postures on a hard

floor surface.

2.3 Pulse Integration

To extract a periodic waveform that is characteristic

for each test subject in each posture and recording

point, we integrate the ΔZ signal in the time domain

by windowing it. The windowing is done by using a

simultaneously recorded ECG signal for timing. The

rising edge of the QRS complex is used to trigger

the start and stop of each window, which are then all

summed together. This helps us to find the periodic

cycles in the sometimes noisy ΔZ signal. From now

on, we refer to them as the standard pulse

waveforms.

The analysis of the standard pulse waveforms is

performed by comparing the signal-to-noise ratios

(SNR) of different test subjects and postures. We are

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Figure 4: ∆Z signal and ECG signal recorded from test subject number 4 (male). The posture was prone and the recording

was performed at abdominal height. Cardiac activity is clearly visible. SNR is 8.64.

Figure 5: ∆Z signal and ECG signal recorded from test subject number 3 (male). The posture was prone and the recording

was performed at abdominal height. Both cardiac activity and respiration are clearly visible. SNR is 3.77.

interested in finding out if some postures are more

favourable than others, and if some people produce

stronger cardiac signals than others. The SNR is

calculated by taking the true rms voltage of a

standard pulse waveform and dividing it by the rms

voltage of the base noise. The base noise is

measured by recording a 20-second sample while the

floor is empty. Correlation coefficients are also

calculated in order to find out if different people

produce similar signals in the same postures.

3 RESULTS

3.1 Raw Signals

Some of the raw samples are presented in Figures 4,

and 5. The first shows clear cardiac activity, and the

latter shows clear respiratory activity. The

simultaneously recorded ECG signals are plotted

under the ΔZ signals. Dashed vertical lines represent

the windowing separators triggered by the rising

edges of the QRS complexes in the ECG signals.

3.2 Integrated Pulses

When the raw ΔZ signals are integrated in time

using the pace provided by the ECG signal, we get

the standard pulse waveforms presented in Figure 6.

The dark grey traces represent individual test

subjects, and the light grey fill colour represents the

variation within the whole test group. The black

trace shows the average within the group.

It seems that the clearest peaks in the averaged

pulses are present in the prone posture. Also the

supine posture at abdominal measurement height

produces clear peaks.

Table 1 shows the SNR values of the standard

pulse waveforms in every posture and at every

recording point. The higher the SNR, the stronger

the cardiac signal. Respiration and other artefacts in

the signal do not affect the SNR value because of the

ECG-based windowing. The averaged SNR of each

test subject is shown in the last row, and the average

of each posture is shown in the right-hand column.

The rms base noise voltage used in the SNR

calculations was 14.03 mV.

The correlation coefficients of the postures and

recording points are presented in Table 2. These

values are averages of the correlation coefficients

between the test subjects in a certain posture and at a

certain recording point.

If the correlation is high, people produce similar

signals in the same posture and at the same

recording point. High correlation can also be seen as

a narrow grey area in the corresponding part of

Figure 6.

It seems that the correlation between the test

subjects is over 50 percent in the prone posture. It is

also noteworthy that the correlation reaches 33

percent in the supine posture at abdominal

measurement height.

BIOSIGNALS WITH A FLOOR SENSOR - Near Field Imaging Floor Sensor Measures Impedance Changes in the Torso

127

Figure 6: The standard ΔZ pulse waveforms of all test subjects in each posture and at each recording point. The black trace

is an average of the test group. The grey fill colour represents the variation within the test group. Note that the y-scale in the

prone postures is larger (from -400 mV to +400 mV) than in the other postures (from -150 mV to +150 mV).

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Table 1: Signal-to-noise ratio in different circumstances.

SNR Test subjects

Position

♀

♂

Average

Prone,

sternal ht.

1.79 1.34 7.92 4.75 3.80

3.92

Prone,

abd. ht.

1.89

0.85 3.77 8.64 3.20

3.67

Supine,

sternal ht.

3.77 1.18 0.96 2.52 1.27

1.94

Supine

abd. ht.

1.68 1.27 2.71 4.11 2.22

2.40

Left lateral,

sternal ht.

2.03 0.58 0.61 2.87 1.93

1.60

Left lateral,

abd. ht.

2.30 1.39 1.71 2.66 2.60

2.13

Right lat.,

sternal ht.

1.38 0.95 2.17 2.92 2.30

1.95

Right lat.,

abd. ht.

1.35 1.24 1.27 2.01 2.46

1.76

Average

2.03 1.10 2.70 3.81 2.47 2.42

Table 2: Correlation between the test subjects.

Posture and recording point Mean correlation

Prone, sternal ht. 0.555

Prone, abdominal ht. 0.500

Supine, sternal ht. 0.043

Supine, abdominal ht. 0.325

Left lateral, sternal ht. 0.093

Left lateral, abdominal ht. 0.265

Right lateral, sternal ht. 0.193

Right lateral, abdominal ht. -0.033

Average 0.243

4 DISCUSSION

The floor sensor produces a cyclic cardiac signal,

which has the same period as a simultaneous ECG

recording. The cardiac signal presumably originates

from changes in the blood concentration in the torso.

The relatively good conductance of blood reduces

the average tissue impedance seen with the sensor

elements. The cardiac waveform recorded with the

floor sensor resembles remotely the ΔZ signal in

impedance cardiography (Patterson, 1989).

However, this similarity is present only in the prone

postures.

The results show that the cardiac signal is clear

when test subjects are lying prone on the sensor

elements. The sternal height recording point seems

to be slightly better than the abdominal height

recording point. The SNR values when they are

lying prone are significantly higher than in other

postures (see grey cells in Table 1). Postures other

than lying prone produce weaker signal amplitudes;

however, they are still mostly above the base noise

(SNR > 1). The two females in this test group had

lower SNR values than the males.

The standard pulse waveforms show a clear

correlation between all the test subjects in both of

the prone recording points. This suggests that this

recording method could be reproducible and that

people produce similar waveforms in the prone

posture. Also the supine posture at abdominal

measurement height produces clear correlation (see

grey cells in Table 2).

In addition to the correlation values, the

recording points and postures have other similarities

to each other. Almost every part in Figure 6 has a

notch or a peak at 100 milliseconds and a second

notch/peak at 200 milliseconds. When observing the

notch/peak at 200 milliseconds, we notice that it

points upwards when the person is lying prone and

downwards when they are lying supine. The

behaviour of the 100-millisecond notch/peak is

similar but inverted.

Respiration is most often visible when the

recording is performed at abdominal height, while

the person is lying prone or supine. This suggests

that the respiratory signal originates from the

movements of the diaphragm. Observing Figure 5,

the respiratory activity seems to be similar to the

respiratory activity in bioimpedance signals recorded

with galvanic electrodes (Vuorela, 2008).

The near field imaging floor sensor under study

can not match the cardiac monitoring distance of the

ultra-high input impedance probes (Harland, 2001).

The body of the person must be in direct contact

with the insulating 3 mm floor covering. The bulk

impedance between the sensor elements of the floor

sensor system is approximately 650 Ω at 90 kHz.

The human body is coupled parallel to these

elements and is dominant compared to the bulk

impedance only at very close ranges. This explains

the low cardiac monitoring distance, which most

probably causes the severe limitations in the

postures. However, as far as we know, there is no

implementation of probes with high input impedance

incorporated in a system capable of tracking people.

The fact that we can track a person and measure

vital signs of a fallen person on an arbitrary point in

the monitored space, makes this a novel method.

5 CONCLUSIONS

The floor sensor system under study was designed to

track people, but also shows promise in vital sign

BIOSIGNALS WITH A FLOOR SENSOR - Near Field Imaging Floor Sensor Measures Impedance Changes in the Torso

129

monitoring. Cardiac activity is clearly visible when a

person is lying prone on the floor.

Waveform correlation between all the test

subjects is clear when the recording posture is prone

(at both measurement heights) or supine (at

abdominal height). Respiration is most often visible

when recording is performed at abdominal height

while the person is lying prone or supine.

Combined with automatic fall detection, the vital

sign monitoring capability would be most useful in

many applications. These could include the care of

the elderly and seclusion monitoring. The limitations

in the favourable recording postures prevent the use

of this vital sign monitoring method in crucial

applications.

Our future work includes development of

algorithms for automatic detection of the best vital

sign recording point using the results obtained from

this study. We also aim to publish our existing

method for automatic fall detection.

ACKNOWLEDGEMENTS

This study was supported by the European Union and the

Jenny and Antti Wihuri Foundation. The authors are also

grateful to UPM Corporate Venturing for providing the

necessary multi-layer thick film sensor laminates.

REFERENCES

Rimminen, H., Linnavuo, M., and Sepponen, R., 2008.

Human Tracking Using Near Field Imaging. In

Proceedings of the Second International Conference

on Pervasive Health, pp. 148-151. ICST.

Prance, R. J., Debray, A., Clark, T. D., Prance, H., Nock,

M., Harland, C. J., and Clippingdale, A. J., 2000. An

ultra-low-noise electrical-potential probe for human-

body scanning. In Measurement Science and

Technology, Volume 11, Issue 3, pp. 291-297. Institute

of Physics Publishing.

Harland, C. J., Clark, T. D., and Prance, R. J., 2001.

Electric potential probes —New Directions in The

Remote Sensing of The Human Body. In

Measurement Science and Technology, Volume 13,

Issue 2, pp. 163-169. Institute of Physics Publishing.

Smith, W. J., and LaCourse, J. R., 2004. Non-Contact

Biopotential Recording from the Human Body Using a

Low-Impedance Charge Amplifier. In Proceedings of

the 30th Annual International Conference on

Bioengineering, pp. 31-32. IEEE.

Alametsä, J., Värri, A., Koivuluoma, M., and Barna, L.,

2004. The Potential of EMFi Sensors in Heart Activity

Monitoring. In 2nd OpenECG Workshop "Integration

of the ECG into the EHR & Interoperability of ECG

Device Systems”, pp. 1.-3.

Korjenevsky, A.V., 2004. Electric field tomography for

contactless imaging of resistivity in biomedical

applications. In Physiological Measurement, Volume

25, Issue 1. pp. 391-401. Institute of Physics

Publishing.

Tuykin, T.S., and Korjenevsky, A.V., 2007. Electric field

tomography system with planar electrode array. In

Proceedings of the 13th International Conference on

Electrical Bioimpedance and the 8th Conference on

Electrical Impedance Tomography, pp. 201-204.

Springer Berlin Heidelberg.

National Semiconductor Corporation, 1969. High Q Notch

Filter. In Linear Brief series, No. 5. National

Semiconductor.

Patterson, R.P., 1989. Fundamentals of impedance

cardiography. In Engineering in Medicine and Biology

Magazine, Volume 8, Issue 1, pp. 35-38. IEEE.

Vuorela, T., Vanhala, J., Seppä, V.-P., Hyttinen, J., 2008.

Two portable long-term measurement devices for ECG

and bioimpedance. In Proceedings of the Second

International Conference on Pervasive Health, pp.

169-172. ICST.

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

130