Comparison of a Custom Functional Near-infrared Spectroscopy

Sensor, a Peripheral SpO2 Sensor, and a Standard Laboratory

Sensor (Biopac) for RR-Interval Assessment

Bethany K. Bracken

, Polemnia G. Amazeen

, Aaron D. Likens

, Mustafa Demir

and Cameron T. Gibbons

Charles River Analytics, 625 Mount Auburn St., Cambridge, MA 02138, U.S.A.

Department of Psychology, Arizona State University, P.O. Box 871104, Tempe, AZ 85287, U.S.A.

Keywords: Functional Near-infrared Spectroscopy (fNIRS), Cognitive Workload, Real-world Environment Sensors.

Abstract: Across many careers, individuals face alternating periods of high and low cognitive workload which can

impair cognitive function and undermine job performance. We have designed and are developing an

unobtrusive system to Monitor, Extract, and Decode Indicators of Cognitive Workload (MEDIC) in real-

world environments. With our partners at Biosignals Plux, we designed and manufactured a functional near-

infrared spectroscopy (fNIRS) device that measures brain blood oxygenation and cardiac information in a

form-factor that can be mounted on the inside of a baseball cap or headband. Because MEDIC is designed to

be used in realistic, sometimes high-motion environments, changes in blood oxygenation to the brain must be

put in context of current levels of physical activity without intruding on the activity of the user. Therefore, we

also developed a NIRS Armband device made up of a combination of Plux sensors including: SpO2 sensor to

measure cardiac information, a galvanic skin response sensor, a 6-axis accelerometer, and a non-contact skin

temperature sensor. Because these were custom sensors, we tested them against a standard laboratory sensor

(a Biopac RSPEC-R) while participants completed an obstacle course of cognitive and physical tasks.

1 INTRODUCTION

Across many careers, individuals face alternating

periods of high and low cognitive workload which

can impair cognitive function and undermine job

performance. We have designed and are developing

an unobtrusive system to Monitor, Extract, and

Decode Indicators of Cognitive Workload (MEDIC)

in real-world environments. With our partners at

Biosignals Plux, we designed and manufactured a

functional near-infrared spectroscopy (fNIRS) device

in a form-factor that can be mounted on the inside of

a baseball cap or headband. fNIRS is useful to detect

blood oxygenation changes associated with cognitive

states of interest, such as cognitive workload

(Tichauer, Hadway, Lee et al., 2005; Keller, Nadler,

Alkadhi et al., 2003). When cognitive workload

increases, there is a corresponding increase in

prefrontal blood oxygenation until the task becomes

too difficult, at which point blood oxygenation

decreases (Bunce, Izzetoglu, Ayaz et al., 2011; Ayaz,

Cakir, Izzetoglu et al., 2012; Ayaz, Shewokis, Bunce

et al., 2012). Because MEDIC is designed to be used

in realistic, sometimes high-motion environments,

changes in blood oxygenation to the brain must be put

in context of current levels of physical activity

without intruding on the activity of the user.

Therefore, we also developed a near infrared

spectroscopy (NIRS) Armband device that includes a

SpO2 sensor to measure cardiac information, a

galvanic skin response sensor, a 6-axis accelerometer,

and a non-contact skin temperature sensor. Because

these were custom sensors, we tested them against a

standard laboratory sensor – a Biopac RSPEC-R–

while participants completed an obstacle course of

cognitive and physical tasks.

2 METHODS

We first designed a forehead sensor device that

includes a custom fNIRS sensor and a three-axis

accelerometer designed to be integrated into a

baseball cap or headband, or standard issue gear such

Bracken, B., Amazeen, P., Likens, A., Demir, M. and Gibbons, C.

Comparison of a Custom Functional Near-infrared Spectroscopy Sensor, a Peripheral SpO2 Sensor, and a Standard Laboratory Sensor (Biopac) for RR-Interval Assessment.

DOI: 10.5220/0006728402810285

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS, pages 281-285

ISBN: 978-989-758-279-0

281

as a helmet or surgeon’s cap. This sensor is more

portable and less obtrusive than most commercially-

available sensors. alone (top left), mounted inside a

helmet (top right), being worn during a jump roping

task (bottom left), and being worn during a medical

training simulation (bottom right).

Figure 1: Custom fNIRS sensor alone (top left), mounted

inside a helmet (top right), worn during jump roping

(bottom left), and worn during a medical training

simulation (bottom right).

Participants wore Charles River Analytics/Plux

sensors and standard sensors (Biopac) while

completing well-validated cognitive tasks, physical

tasks, and combinations of cognitive and physical

tasks. This allowed us to assess the accuracy of

Charles River Analytics/Plux sensors (by comparing

them to Biopac data).

The evaluation of this sensor suite included 21

teams of three undergraduates completing physical

and cognitive challenges. (1) Baseline involved

sitting quietly. (2) Word list memorization (Miller,

1956) required participants to remember as many

words as possible. (3) Balance board required

participants to coordinate rolling a ball edge to edge

on a large, flat, weighted board without dropping it

for a specified amount of time. (4) For twenty

questions (Denney, 1987), participants asked yes-or-

no questions (up to 20) of the experimenter to identify

a pre-specified object. (5) For the puzzle task

(Shepard & Metzler, 1971; Guastello et al., 2014),

participants put together standard cardboard or plastic

puzzles of varying difficulty. (6) For hot potato,

participants each maintained balance on a BOSU ball

while passing weighted (medicine) balls from one

individual to the next. (7) For logic problems (Braine,

1990), individuals were given logic puzzles to solve

(e.g., http://www.brainbashers.com/logic.asp). (8)

For moving boxes (Amazeen, 2013), participants

lifted and moved boxes of variable weights and sizes

to construct a wall. (9) For jump rope, participants

jumped synchronously to complete a specified

number of consecutive jumps.

3 RESULTS

Sixty-three participants were recruited from Arizona

State University (ASU) and surrounding areas in

Mesa to participate in a study examining team

coordination. Informed consent was obtained prior to

the start of the experimental session. Each participant

received $20 upon completion of the experimental

session. The experimental protocol was approved by

ASUs Institutional Review Board and participants

were treated in accordance with the ethical guidelines

of the American Psychological Association.

Participants were grouped into three-member

teams, for a total of 21 teams. Data from seven teams

were removed from analysis due to logistical (e.g.,

incomplete teams) and technical (e.g., equipment

failure) difficulties. The following results comprise

data from the remaining 14 teams (see Table 1).

Table 1: Sample demographics. Mean (standard deviation).

Team

Male/

Female

Age (years)

Weekly Exercise

(hours)

1/2

26.7 (5.5)

4.7 (2.5)

3/0

24.0 (1.7)

5.7 (1.5)

2/1

23.3 (0.6)

5.3 (2.3)

2/1

24.3 (2.9)

4.2 (1.4)

3/0

21.7 (2.5)

6.0 (5.3)

3/0

24.3 (0.6)

6.7 (2.1)

2/1

27.7 (6.4)

9.0 (9.6)

3/0

23.7 (0.6)

5.3 (1.5)

3/0

23.7 (1.2)

1.3 (1.5)

2/1

25.3 (2.1)

7.7 (2.1)

2/1

22.3 (0.6)

8.3 (5.1)

2/1

25.3 (0.6)

4.8 (2.0)

3/0

26.3 (0.6)

4.7 (2.5)

2/1

22.3 (0.6)

8.8 (5.4)

Various physiological measures were collected

from each participant. Plux sensors were positioned

on the non-dominant arm (i.e., Armband) and

forehead (i.e., fNIRS Device) of each of the three

participants. A Biopac wireless ECG transmitter

(Biopac Systems Inc., Goleta, California, USA) was

used to collect electrocardiogram (ECG) data from

two of the three participants. Output from the Biopac

transmitter was transmitting in real time to a PC and

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282

recorded at 1000 Hz using AcqKnowledge software

(Biopac Systems Inc.). ECG signals were filtered and

down-sampled to 250 Hz for later calculation of RR

interval, the time (sec) between two consecutive QRS

complexes, using MATLAB (Mathworks, Inc.).

The experimental session consisted of one four-

minute baseline and nine two-minute cognitive and

physical tasks. Teams completed the baseline once, at

the beginning of the experimental session. They then

completed two repetitions of the coordination task

sequence. Experimental sessions lasted

approximately 75 minutes.

3.1 Signal Comparison

Figure 1, Figure 2, and Figure 3 depict the RR interval

time series from the Charles River Analytics/Plux

fNIRS device (red line) and Biopac transmitter (blue

line) for one participant over the entire experimental

session. Time series’ were smoothed using a 10-

(Figure 1), 20- (Figure 2), and 30-point (Figure 3)

moving average. Across all figures, the measured

heart beat was similar for both devices. Fluctuations

in RR interval can be seen as the participant’s heart

rate oscillates between physical (smaller RR

interval/higher heart rate) and non-physical tasks

(larger RR interval/lower heart rate). To determine

the relationship between the two time series (Charles

River Analytics/Plux fNIRS, Biopac), we computed

the cross-correlation (r) using the “crosscorr”

function in MATLAB. Correlations are depicted in

the bottom left region of each figure. Examination of

those correlations reveals stronger relationships

between the data sets for the 30 second window size.

This trend is also observed in Table 2. This suggests

that a 30 second window is sufficient to preserve and

enhance the dominant (slower) frequencies of the

participant’s RR interval_signal during physical and

non-physical tasks.

Figure 1: Charles River Analytics/Plux fNIRS device (red)

and Biopac (blue) RR interval averaged across 10s

windows.

Figure 2: Charles River Analytics/Plux fNIRS device (red)

and Biopac (blue) RR interval averaged across 20s

windows.

Figure 3: Charles River Analytics/Plux fNIRS device (red)

and Biopac (blue) RR interval averaged across 30s

windows.

Note that the correlations in Table 2 are small but

positive, indicating that the sensors are picking up on

similar information, but there is weak

correspondence.

Table 2: Cross-correlation (mean ± standard deviation) for

each window size.

Window Size

(sec)

Armband–

Biopac

fNIRS Device –

Biopac

0.188 ± 0.180

0.211 ± 0.196

0.206 ± 0.189

0.230 ± 0.211

0.228 ± 0.195

0.249 ± 0.227

Figure 4, Figure 5, and Figure 6 depict RR

interval data from the Armband (red line) and

corresponding Biopac transmitter (blue line) from the

same participant in Figure 1, Figure 2, and Figure 3.

For all figures, we constrained the RR interval scale

from 0 to 4 so that fluctuations in the Biopac signal

could still be seen. However, it should be noted that

RR intervals sometimes extended well past 4,

meaning that the time between heart beats was 4

Comparison of a Custom Functional Near-infrared Spectroscopy Sensor, a Peripheral SpO2 Sensor, and a Standard Laboratory Sensor

(Biopac) for RR-Interval Assessment

283

seconds. This is obviously unrealistic. This artifact

existed across three types of signals (Armband,

fNIRS Device, Biopac sensor) but was most

problematic with Armband data, as can be seen in

lower correlations for Armband and Biopac data than

fNIRS Device and Biopac data in Table 2.

Figure 4: Plux Armband and Biopac RR interval averaged

across 10 second windows.

Figure 5: Plux Armband and Biopac RR interval averaged

across 20 second windows.

Figure 6: Plux Armband and Biopac RR interval averaged

across 30 second windows.

3.2 Task Evaluation

To determine whether the tasks had an effect on heart

rate behaviour, we examined the average RR interval

during the performance of each task in Trials 1 and 2

separately. Figure 7 and Figure 8 depict the RR

intervals from Biopac for two participants in Teams

18 as a function of the task. As expected, participants

exhibited an increase in heart rate (indicated by a

lower RR interval) for the physically demanding tasks

(e.g., moving boxes) compared to the cognitive tasks

(e.g., puzzle). Because the sequence of the tasks

alternated between cognitive and physical tasks, we

can see the heart rate oscillate as a function of the task

demands. The same pattern was observed during trial

2, along with lower overall RR interval values (i.e.,

higher heart rate). Even though participants were

given time to rest in between trials, heart rate never

fully returned to Baseline.

Figure 7: Mean RR interval as a function of task in (black

bars) trial 1 and (grey bars) trial 2.

Figure 8: Mean RR interval as a function of task in (black

bars) trial 1 and (grey bars) trial 2.

The same pattern was observed across all teams,

as seen in the group averages of Figure 3.

Real 2018 - Special Session on Assessing Human Cognitive State in Real-World Environments

284

Figure 9: Mean RR interval as a function of task in (black

bars) trial 1 and (grey bars) trial 2 across all teams.

4 CONCLUSIONS

Participants wore Charles River Analytics/Plux

sensors and standard sensors (Biopac) while

completing well-validated cognitive tasks, physical

tasks, and combinations of cognitive and physical

tasks. This allowed us to assess the accuracy of

Charles River Analytics/Plux sensors (by comparing

them to Biopac data). The evaluation of this sensor

suite included 21 teams of three students completing

physical and cognitive challenges. Various

physiological measures were collected from each

participant.

The correlations in RR interval between the

fNIRS device, Armband device, and Biopac sensor

are small, but positive, indicating that the sensors are

picking up on similar information, but there is weak

correspondence.

The physical and cognitive tasks had very

different effects on heart rate. As expected, the ECG

signal was much more variable during the completion

of the physical tasks, including movement between

stations of the experiment. Occasionally, a sensor

might be sufficiently jostled, particularly in the rope

jumping task, or it might fall off. In those situations,

the signal became very noisy, which made the QRS

complex difficult to resolve. The consequence was

that the peak-picking algorithm might skip relevant

peaks in the signal and estimate an inflated RR

interval (e.g. RR interval>2 sec).

ACKNOWLEDGEMENTS

This material is based upon work supported by the

United States Army Medical Research and Materiel

Command under Contract No. W81XWH-14-C-

0018. Any opinions, findings and conclusions or

recommendations expressed in this material are those

of the author(s) and do not necessarily reflect the

views of the United States Army Medical Research

and Materiel Command. In the conduct of research

where humans are the participants, the investigators

adhered to the policies regarding the protection of

human participants as prescribed by Code of Federal

Regulations (CFR) Title 45, Volume 1, Part 46; Title

32, Chapter 1, Part 219; and Title 21, Chapter 1, Part

50 (Protection of Human Participants).

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(Biopac) for RR-Interval Assessment

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