ORIGINAL RESEARCH
Validation of a novel wearable, wireless technology to
estimate oxygen levels and lactate threshold power in the
exercising muscle
Parisa Farzam, Zack Starkweather & Maria A. Franceschini
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
Keywords
Athletic exercise training, blood lactate,
muscle oxygen saturation, near-infrared
spectroscopy (NIRS).
Correspondence
Parisa Farzam, Athinoula A. Martinos Center
for Biomedical Imaging, Massachusetts
General Hospital, Harvard Medical School
149, 13th Street, Boston, MA 02129.
Tel: +1- 617-726-9338
Fax: +1- 617-726-7422
E-mail: pfarzam@mgh.harvard.edu
Funding Information
This research was sponsored by Dynometrics,
Inc.
Received: 20 February 2018; Accepted: 23
February 2018
doi: 10.14814/phy2.13664
Physiol Rep, 6 (7), 2018, e13664,
https://doi.org/10.14814/phy2.13664
Abstract
There is a growing interest in monitoring muscle oxygen saturation (SmO2),
which is a localized measure of muscle oxidative metabolism and can be
acquired continuously and noninvasively using near-infrared spectroscopy
(NIRS) methods. Most NIRS systems are cumbersome, expensive, ﬁber cou-
pled devices, with use limited to lab settings. A novel, low cost, wireless, wear-
able has been developed for use in athletic training. In this study, we evaluate
the advantages and limitations of this new simple continuous-wave (CW)
NIRS device with respect to a benchtop, frequency-domain near-infrared spec-
troscopy (FDNIRS) system. Oxygen saturation and hemoglobin/myoglobin
concentration in the exercising muscles of 17 athletic individuals were mea-
sured simultaneously with the two systems, while subjects performed an incre-
mental test on a stationary cycle ergometer. In addition, blood lactate
concentration was measured at the end of each increment with a lactate ana-
lyzer. During exercise, the correlation coefﬁcients of the SmO2 and hemoglo-
bin/myoglobin concentrations between the two systems were over 0.70. We
also found both systems were insensitive to the presence of thin layers of vary-
ing absorption, mimicking different skin colors. Neither system was able to
predict the athletes’ lactate threshold power accurately by simply using SmO2
thresholds. Instead, the proprietary software of the wearable device was able
to predict the athletes’ lactate threshold power within half of one power incre-
ment of the cycling test. These results indicate this novel wearable device may
provide a physiological indicator of athlete’s exertion.
Introduction
Quantifying how muscles respond to physical exercise is
of great interest to athletes for improving performance
and mitigating the risk of injury. Traditionally, athletes
rely on measurements such as heart rate, blood lactate
concentration, or maximum oxygen uptake (VO2max).
These parameters are used to determine the intensity
levels at which athletes should be exerting themselves to
maximize athletic performance (Seiler and Kjerland 2006;
Bentley et al. 2007; Esteve-Lanao et al. 2007; Goodwin
et al. 2007). Although heart rate, blood lactate concentra-
tion, and VO2max can help guide an athlete’s training
regimen, these measurements are indicative of systemic
changes occurring in the body, with no speciﬁc informa-
tion about the working muscles.
Interest in examining muscle oxygen saturation (SmO2)
has been growing due to its ability to provide a localized
measurement continuously and noninvasively using near-
infrared spectroscopy (NIRS) (Chance et al. 1992; Belar-
dinelli et al. 1995; Hamaoka et al. 1996, 2011; Bhambhani
et al. 1997; Grassi et al. 1999, 2003; Wang et al. 2006;
Soller et al. 2008; Bailey et al. 2009; Ihsan et al. 2013;
Racinais et al. 2014; Boone et al. 2016; van der Zwaard
et al. 2016; Baker et al. 2017; Hammer et al. 2018; Perrey
and Ferrari 2018). NIRS techniques work by delivering
ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
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2018 | Vol. 6 | Iss. 7 | e13664
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Physiological Reports ISSN 2051-817X
light (in the 650–900 nm wavelength range) into the tis-
sue and measuring the diffused light to estimate the
absorption and scattering properties of the measured tis-
sue volume (Yodh and Chance 1995). The concentrations
of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR)
in the tissue can be estimated from the measured absorp-
tion spectrum. Muscle oxygen saturation, also referred as
muscle oxygenation (SmO2), is then calculated by taking
the ratio of HbO to total hemoglobin concentration
(HbT). Previous studies have investigated the HbO, HbR,
HbT, or SmO2 trends during exercise to determine if
these NIRS parameters provide useful information to
guide athletic training (Belardinelli et al. 1995; Bhamb-
hani et al. 1997; Grassi et al. 1999, 2003; Wang et al.
2006; Bailey et al. 2009; Hamaoka et al. 2011; Racinais
et al. 2014). Correlations have been observed between the
threshold power found from VO2max or lactate data and
NIRS methods (Belardinelli et al. 1995; Grassi et al. 1999;
Wang et al. 2006; van der Zwaard et al. 2016). These
NIRS studies developed techniques to ﬁnd an athlete’s
threshold power by analyzing the trends in HbO, HbR,
HbT, or SmO2 after an incremental power test (Belar-
dinelli et al. 1995; Grassi et al. 1999; Wang et al. 2006;
van der Zwaard et al. 2016). Other work has been per-
formed to understand muscle adaptations throughout an
athlete’s training by examining the HbO, HbR, HbT, or
SmO2 kinetics during speciﬁc exercise protocols (Bailey
et al. 2009; Ihsan et al. 2013). These studies support the
incorporation of NIRS technology into athletic training
but are limited to the laboratory setting due to the
required optical ﬁbers, probes, and larger instruments to
obtain the measurements. The noninvasive monitoring of
muscle oxygenation could signiﬁcantly beneﬁt from small,
wearable, wireless, and accurate devices that can deliver
real-time feedback to athletes.
Currently, there are a small number of wearable, ﬁber-
less, NIRS devices used in the athletic market, such as the
Portamon (Artinis Medical System, Einsteinweg, The
Netherlands),
Moxy
Monitor
(Fortiori
Design,
LLC,
Hutchinson, MN, USA), and BSX Insight (BSX Athletics,
Austin, TX, USA). A review from Perrey and Ferrari
(2018) goes into detail about the different studies that
have examined the use of these NIRS devices in athletic
training and SmO2 monitoring. The Portamon and Moxy
devices can be manually strapped on to any muscle group
and have been used during a variety of activities, includ-
ing cycling, running, and strength training (Perrey and
Ferrari 2018). The BSX Insight is designed to be worn on
the gastrocnemius muscle within a custom-made com-
pression sleeve and differentiates itself by providing ath-
letes with their lactate threshold when following a given
protocol (Borges and Driller 2016; Driller et al. 2016).
These studies on wearable NIRS devices in the athletic
community show value in obtaining optical measure-
ments
during
sports,
however
the
validity
of
measurements from wearables would be enhanced by a
direct comparison with a bench-top, ﬁber-based, FDNIRS
system.
In this study, we compared the SmO2 of the quadriceps
muscle group recorded by a wearable, low-cost, continu-
ous-wave (CWNIRS) consumer device (Humon Beta,
Dynometrics, Inc.) against a benchtop ﬁber-based fre-
quency-domain near-infrared spectroscopy (FDNIRS) sys-
tem (MetaOx, ISS, Champaign, IL). The FDNIRS system
is considered the most robust and reliable reference com-
mercially available for comparison of oximeters (Kleiser
et al. 2016). Our goal was to examine the accuracy of this
wearable device and understand the limitations that arise
when using CWNIRS for muscle oxygenation measure-
ments. We also investigated the real-time feedback from
the Humon Beta device and reported the differences
between the optically derived threshold and blood lactate
threshold during an incremental cycling test.
Methods
Study population and measurement
protocol
Fifteen male and three female athletic subjects performed
an incremental step test on the cycle ergometer. For logis-
tical reasons, the test was carried out with the MetaOx
probe on the right leg rectus femoris and the Humon
Beta on the left leg rectus femoris, as illustrated in Fig-
ure 1. The rectus femoris was chosen as the muscle to
monitor in order to minimize ﬁber movement since this
was the area of the leg where the ﬁbers could remain
most stable. Each session began with baseline measure-
ments for approximately two minutes, where subjects
were instructed to stay as still as possible. Following a
well-established incremental test protocol (Madden et al.
2013), the subjects began cycling at 30 W for 4 min, and
the power was increased in 30 W increments every 4 min
until voluntary exhaustion. Voluntary exhaustion was
determined when the subject requested to stop or when
he/she could no longer maintain the cycling cadence. In
the last minute of each power interval, the blood lactate
concentration was measured using a handheld lactate ana-
lyzer (Lactate Plus meter, Nova Biomedical, Waltham,
MA) by averaging three repeated blood samples. The sub-
jects were asked to keep a consistent cadence throughout
the cycling protocol, which was typically between 80 and
100 rotations per minute (RPM). For each subject, the
lactate threshold power was determined as the cycling
power at which the athlete’s blood lactate concentration
reaches 4 mmol/L (Faude et al. 2009; Madden et al.
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ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
A New Wearable NIRS to Estimate Lactate Threshold
P. Farzam et al.
2013). One male subject was excluded from all data anal-
yses because of a Humon Beta malfunction. On this sub-
ject, sweat entered into the sensor and corrupted the
data, however, we veriﬁed that sweat did not enter on
any other Humon Beta device and did not affect any
other measurements. Thus, from now on, we just report
the values of the 17 subjects. In seven of the subjects, two
Humon Beta sensors were worn on the left rectus femoris
muscle to examine the heterogeneity of the measurements
within the same muscle. The devices were placed 2 cm
apart from one another, where one was proximal and the
other was distal along the muscle and the variability due
to location placement was assessed. We veriﬁed the sepa-
ration of the two devices was sufﬁcient to prevent any
light leakage between them.
Before beginning the cycling on an upright stationary
cycle ergometer (Model E3, Kettler USA), basic informa-
tion was collected for each subject. This information
included age, height, weight, and gender. Body Mass
Index (BMI) was calculated as (kg/m2). In addition, the
subcutaneous adipose tissue thickness (SCATT) on the
rectus femoris of the right quadriceps was measured for
each subject using a skinfold caliper before the start of
the cycling test. Information about the subjects can be
found in Table 1. We also tested the vitals (heart rate,
blood pressure, body temperature, arterial oxygen satura-
tion) of all subjects to ensure that the subjects were
healthy prior to the onset of exercise.
The study protocol was reviewed and approved by the
Institutional Review Board (IRB) for Partners Healthcare,
the Partners Human Research Committee (PHRC). The
study method was designed and carried out in accordance
with PHRC requirements and the regulations that govern
human subjects research. All subjects interested in partici-
pating in the study went through a prescreening process
to ensure they were eligible. These subjects all exercised
more than 3 times per week, each session over 30 min
and their exercise routine typically included biking, run-
ning, swimming, or rowing. We refer to this population
as athletes throughout the text. They were all comfortable
exercising at a high intensity for a long period of time.
All eligible participants read and signed the approved
informed consent form before starting the measurement
session.
Instrument setup
The Humon Beta device (Fig. 1) uses two light sources in
the NIR window and three photodetectors to measure the
intensity of the light that has propagated through the tis-
sue. The sources and detectors are found behind individ-
ual polycarbonate windows, which come in contact with
the skin of the user. The photodetectors are located at
distances of 1.2, 1.8, and 2.4 cm from the light sources.
The acquisition rate is set to 4 Hz. The Humon Beta is
6.0 9 5.7 9 1.4 cm in size and has a slight curvature in
the plastic case to allow for easy contact with the skin on
Figure 1. The schematic of the measurement on an upright
stationary ergometer. The top drawing displays the location of
Humon wearable on the left leg and MetaOx probe on the right
leg. The distribution of the sources and detectors are presented for
both the MetaOx probe (source–detector separations: 1.5, 2.0, 2.5,
and 3.0 cm) and Humon Beta wearable (source–detector
separations: 1.2, 1.8, and 2.4 cm) in the bottom images.
Table 1. Subjects demographic.
All
Males
Females
Number
17
14
3
Right-footed
15
12
3
Age (years)
31  6
30  6
35  5
Weight (kg)
71  14
75  11
51  3
Height (cm)
176  11
180  7
156  3
BMI (kg/m2)
23  3
23  3
21  2
SCATT (mm)
5  2
5  2
7  1
Details about the subjects’ footedness, age, weight, height, BMI,
and subcutaneous adipose tissue thickness (SCATT) are shown,
including a gender breakdown for each category.
ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
2018 | Vol. 6 | Iss. 7 | e13664
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P. Farzam et al.
A New Wearable NIRS to Estimate Lactate Threshold
the quadriceps. The wearable is attached to the athlete’s
quadriceps, using a strap that hooks through the device
and can be secured around the thigh with a hook-and-
loop fastener. The Humon Beta communicates with a
smartphone via Bluetooth, and a custom app displays the
workout progress in real time.
MetaOx is a hybrid device that consists of FDNIRS to
measure hemoglobin concentration and tissue oxygena-
tion and diffuse correlation spectroscopy (DCS) to mea-
sure blood ﬂow (Boas et al. 2016). The details of the
system and data analysis are described by Carp et al.
(2017). For this work, we only considered the FDNIRS
data even though DCS data were also acquired. In sum-
mary, the FDNIRS components include 8 lasers in the
visible and NIR spectral region (672, 726, 759, 813, 690,
706, 784, and 830 nm) modulated at 110 MHz and 4
photomultiplier tubes detectors (PMT) modulated at
110.005 MHz to achieve heterodyne detection at 5 kHz.
The lasers are rapidly multiplexed in sequence and allow
the fast measurement (10 Hz) over all wavelengths. The
light is delivered to the tissue through a ﬁber optics bun-
dle coupled to the probe. The diffused photons are col-
lected by the probe’s ﬁber optics bundles located at
distances of 1.5, 2.0, 2.5, and 3.0 cm from the source (q),
(see Fig. 1) and delivered to the four PMTs.
Data analysis
The Humon Beta and MetaOx data were coregistered and
acquired continuously for the whole duration of the pro-
tocol. For MetaOx data analysis, the detected light ampli-
tude (AC) and phase shift at four separations and eight
wavelengths are calibrated on a silicone tissue mimicking
phantom with known optical properties to account for
the different gains of the four detectors (Farzam et al.
2013). After calibration, the frequency-domain solution of
the photon diffusion equation in the semi-inﬁnite geome-
try is used to ﬁt for the wavelength-dependent optical
properties of the tissue (Durduran et al. 2010). At each
wavelength, a linear ﬁt is performed on (ln(AC(k)q2))
and phase over source-detector distances. Absorption (la)
and reduced scattering (ls0) coefﬁcients at each wave-
length are calculated from the slopes of the linear ﬁts
(Fantini et al. 1995). The measured absorption coefﬁcient
is sum of the absorption of tissue chromophores (la (k)
= ∑ ei(k)ci), where the wavelength-dependent extinction
coefﬁcient, ei(k), of the ith chromophore is obtained from
the literature (Prahl), and ci is the concentration of the
ith chromophore. The primary muscle chromophores in
the near-infrared are water, oxy-, deoxy-hemoglobin, and
myoglobin.
The
muscle
tissue
water
percentage
is
assumed to be 75% (Franceschini et al. 1997). Since myo-
globin cannot be distinguished from hemoglobin due to
the spectral overlap (Quaresima et al. 2004), its contribu-
tion
is
combined
into
the
calculated
oxy-
and
deoxy-hemoglobin
concentrations.
When we refer
to
hemoglobin throughout the text, we acknowledge that
this is a combination of hemoglobin and myoglobin. The
total hemoglobin concentration and oxygen saturation are
calculated as HbT = HbO + HbR and SmO2 = HbO/
HbT, respectively.
The Humon Beta is a CW device and only measures
light intensity at three separations and two wavelengths.
Hence, to estimate hemoglobin concentration and oxy-
genation, it needs to assume a ﬁxed scattering coefﬁcient.
The algorithms used to recover hemodynamic parameters
were not disclosed and are proprietary to Dynometrics
Inc. The HbO, HbR, HbT, and SmO2 results obtained
with the two devices were compared to determine the
drawbacks of using a limited set of wavelengths and ﬁxed
scattering coefﬁcients for a small, inexpensive wireless
NIRS device which cannot include as many features as
the benchtop frequency-domain system.
In addition, to further estimate the error introduced by
the simpliﬁcations in the wearable system, we compared
full MetaOx results with estimates obtained, using a sub-
set of the MetaOx intensity data, which best matches the
Humon Beta separations and wavelengths. In this way, we
can isolate the errors due to the reduced dataset from
contamination arising from other parameters such as dif-
ferences in sensor location and differences between left
and right leg muscles.
To test the effect of superﬁcial thin layers, such as dif-
ferent skin tones, on the data collected by the Humon
Beta and MetaOx systems, neutral density (ND) ﬁlters
were placed over a silicone tissue mimicking phantom
with known optical properties and data were collected
with the two systems. We evaluated how the presence of
these superﬁcial thin layers affect the recovered “effective
SmO2”, both with the Humon Beta and the MetaOx. We
call the calculated parameter “effective SmO2”, since the
phantom is made of silicon and does not contain blood.
For the MetaOx we also evaluated how the ND ﬁlter
affected the measured intensity and the slope of both
phase and intensity versus distance.
Moreover, in the 17 subjects we evaluate the effect of
subcutaneous adipose tissue on the estimated HbO, HbR,
HbT, and SmO2 by analyzing the MetaOx cycling data
using only the shorter (1.5 and 2.0 cm) or the larger (2.5
and 3.0 cm) separations. We estimated the difference in
the recovered HbO, HbR, HbT, and SmO2 at the two
separations throughout the whole cycling test, and the
correlation between SmO2 drop and measured SCATT.
Finally, we examined the relationship between the mea-
sured hemodynamic parameters and the blood lactate
measures and tested the accuracy of the Humon Beta for
2018 | Vol. 6 | Iss. 7 | e13664
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ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
A New Wearable NIRS to Estimate Lactate Threshold
P. Farzam et al.
predicting the lactate threshold power. The Humon Beta
employs a proprietary algorithm to determine the lactate
threshold in real time. The data are processed and dis-
played on a smartphone application, where in addition to
SmO2 values, a corresponding exercise zone is shown.
Four zones are used to classify the subjects exercise state:
(1) the green zone represents a steady state, (2) the
orange zone indicates the athlete is approaching their
limit, (3) the red zone shows the athlete has hit/exceeded
their limit, and (4) the blue zone means the athlete is in
a recovery phase. For this analysis, we excluded the three
female subjects since the power increments were too large,
which resulted in the lactate threshold power and the end
of the exercise to be too close in time.
For HbO, HbR, HbT, and SmO2 comparisons between
the MetaOx and Humon Beta, we report the Pearson cor-
relation coefﬁcient and the relative root mean square
error (RMSE). Nonparametric statistical tests were per-
formed when comparing features in the SmO2 curves
between the MetaOx and Humon Beta. All the data pro-
cessing and statistical analyses in this paper are performed
using MATLAB (MathWorks, USA), version R2017a, and
Statistics and Machine Learning Toolbox, version 11.1.
Results
Optical and physiological properties of the
leg muscle
Figure 2 shows the average of optical properties, that is,
the absorption coefﬁcient (la) and reduced scattering
coefﬁcient (ls0), measured by MetaOx across all the sub-
jects over 2 min of baseline before starting to pedal. The
error bars indicate the mean over all subjects and 95%
conﬁdence interval of the mean. The dashed red line is
the ﬁtted spectrum and the gray shaded area indicates the
95% conﬁdence interval of the ﬁtted spectrum. The
resulting average oxy- and deoxy-hemoglobin concentra-
tions are 37  12 (lmol/L) and 19  6 (lmol/L), respec-
tively. For the scattering, the average number density (a)
is 8.54  1.4 and the average effective particle size (b) is
0.75  0.24, for the relationship derived from the Mie
model (Jacques 2013).
The dashed red line is the mean of ﬁtted spectrum and
the gray shaded area indicates the 95% conﬁdence inter-
val of the mean.
SmO2 trend during cycling incremental
power test
We tested the accuracy of the HbO, HbR, HbT, and
SmO2 estimations from the Humon Beta data against the
MetaOx measurements. The data from all 17 subjects are
included for these calculations. A representative dataset
from the incremental power test is shown in Figure 3,
where the change of SmO2 over time is presented for
both MetaOx (dotted curve) and Humon Beta (solid
curve). The vertical lines indicate the time point that the
power on the bike was changed and the numbers at the
top of the plot between the lines show the power (Watts)
at which the subject was cycling. In general, at the begin-
ning of the exercise, the muscle oxygen saturation slightly
Figure 2. The measured absorption coefﬁcient (la) and reduced scattering coefﬁcient (ls’) of rectus femoris muscle, and their ﬁtted spectrum.
The red error bars indicate the mean over all subjects and 95% conﬁdence interval of the mean. The dashed red line is the ﬁtted spectrum and
the gray shaded area indicates 95% conﬁdence interval of the ﬁtted spectrum.
ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
2018 | Vol. 6 | Iss. 7 | e13664
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P. Farzam et al.
A New Wearable NIRS to Estimate Lactate Threshold
increases. As the power increases and the subject under-
goes high exertion levels, the oxygen saturation starts to
decrease. Finally, SmO2 sharply increases when the subject
starts the recovery phase.
Figure 4 is a scatter plot of the SmO2 values measured
by the two devices in all subjects during cycling. The solid
black line in Figure 4 indicates a line with slope of one
and zero intercept, which represents the trend if there
was a 1:1 relationship between the measured SmO2 by
Humon Beta and the MetaOx. The dashed line shows the
best linear ﬁt to the data, with an intercept of ~10 and
R = 0.74. This linear relationship was found to be signiﬁ-
cant (P value < 0.001 for slope and intercept). With
respect to the MetaOx, the Humon Beta overestimates
(2–3%) SmO2 at low values and underestimates (1%)
SmO2 at high values.
Quantifying differences between Humon
Beta and MetaOx measurements
The results of the comparison between Humon Beta and
MetaOx, MetaOx-subset and MetaOx, and Humon Beta
at two locations are reported in Table 2. In all three cases,
the correlation coefﬁcient and the relative RMSE between
two measures are reported for HbO, HbR, HbT, and
SmO2, averaged over all subjects.
The results show a good agreement between Humon
Beta and MetaOx data. The differences are larger for
HbO, HbR, and HbT (19.4%, 25.7%, and 20.7%, respec-
tively) and smaller for SmO2 (3.4%). While the absolute
hemoglobin concentration values are different, a strong
correlation (correlation coefﬁcient: 0.72-0.86) is present
between the time traces measured with the Humon Beta
and the MetaOx.
The MetaOx-subset assumes the same ﬁxed scattering
coefﬁcient and similar wavelengths and separations as the
Humon Beta. Table 2 shows that the errors due to the
simpliﬁed model results in a small deviation from the full
MetaOx
data.
The
average
relative
RMSE
between
MetaOx-subset
and
MetaOx
are
14.7%,
and
14.7%,
12.8%, for HbO, HbR, and HbT, respectively, and 3.6%
for SmO2 (correlation coefﬁcient: 0.83–0.96).
To estimate differences due to differences in probe
location on the same rectus femoris, the data from the
two Humon Betas worn on the same leg (7 subjects) are
compared and the results are shown in Table 2, Humon
location 1 vs. Humon location 2.
Features within the SmO2 curves were also compared
between the MetaOx and Humon Beta measurements for
the 14 male subjects. Table 3 reports the average SmO2%
drop throughout the cycling tests, the average time at
which 50% of the recovery occurred, and the average
SmO2% overshoot relative to the average starting value
during the ﬁrst power increment (30 W). A Wilcoxon
rank sum test was performed between the MetaOx and
Figure 3. SmO2 results for a representative subject (subject #4)
during the incremental cycling test. Humon Beta SmO2 (solid line)
and MetaOx SmO2 (dashed line) absolute values are 3–5% different
but the lines closely follow each other for the whole duration of
the exercise. The vertical lines indicate the different cycling power
periods with the power level indicated on the top.
Figure 4. : Scatter plot of the SmO2 values measured by Humon
Beta (y-axis) and MetaOx (x-axis) during exercise for all subjects.
Data were down sampled to 12 sec per point, to simplify the
ﬁgure. A strong linear relationship can be observed between the
SmO2 measured by both systems. The equation and correlation
coefﬁcient of the best linear ﬁt (dashed line) are reported in the
ﬁgure.
2018 | Vol. 6 | Iss. 7 | e13664
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ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
A New Wearable NIRS to Estimate Lactate Threshold
P. Farzam et al.
Humon Beta data and the p values shown in the ﬁnal
row
of
Table 3
indicate
that
these
values
are
not
signiﬁcantly
different
between
the
two
systems
(P
values > 0.05).
Determining the lactate threshold power
The SmO2 data for the 14 male subjects were analyzed to
determine if the lactate threshold power could be found
using MetaOx and Humon Beta SmO2 changes. Neither
an SmO2 absolute threshold value or relative threshold
drop could identify the lactate threshold power accurately.
In particular, due to the large variability in SmO2 values
across subjects, we could not ﬁnd an absolute threshold
value that could work for all subjects. Also, the difference
between maximum SmO2 and SmO2 at 4 mmol/L lactate
concentration (SmO2-drop) varied a lot across subjects. At
best, with either the MetaOx or the Humon Beta, by set-
ting the threshold drop equal to the average SmO2-drop
across subjects, we obtained an average power difference
with respect to the blood lactate measure of about 40 W
and a delay of about 5 min.
Instead, the Humon Beta proprietary algorithm used to
determine the lactate threshold showed good agreements
with the blood lactate measurements. Representative cases
of the Humon Beta SmO2 with the four exercise zones are
shown for subjects one and two in Figure 5. The beginning
of the red zone should correspond to the 4 mmol/L lactate
concentration threshold. While the Humon Beta algorithms
work well for subject #2, there is a relatively large delay on
the estimate of lactate power in subject #1. We evaluated the
accuracy of this threshold with respect to the blood lactate
concentration measures in all 14 male subjects.
Table 4 shows the difference between the threshold
power found by the blood lactate measurement (interpo-
lating the data acquired every 4 min) and the threshold
power determined by the Humon Beta proprietary algo-
rithm in the 14 male subjects. The time difference
between the two thresholds is also reported. Since the
power increments during the cycling test are 30 W, the
Humon Beta identiﬁed 4 threshold powers correctly and
was off by 1 power increment (30 W) in 10 subjects. The
absolute average power difference is 21.4 W and the abso-
lute average time difference is 2:32 min.
Table 2. Results comparisons.
Comparison
Parameter
Correlation coefﬁcient
Relative RMSE (%)
Humon vs. MetaOx
HbO
0.80  0.21
19.4  14.4
HbR
082  0.19
25.7  17.6
HbT
0.72  0.33
20.7  15.6
SmO2
0.86  0.10
3.4  0.9
MetaOx-subset vs. MetaOx
HbO
0.89  0.10
14.7  10.9
HbR
0.96  0.08
14.7  9.9
HbT
0.83  0.16
12.8  8.7
SmO2
0.98  0.02
3.6  1.9
Humon loc.1 vs.Humon loc. 2
HbO
0.86  0.15
22.4  15.1
HbR
0.85  0.16
16.4  10.8
HbT
0.92  0.03
20.4  14.6
SmO2
0.87  0.14
3.6  3.0
The average of correlation coefﬁcients and relative root mean square error (RMSE) of HbO, HbR, HbT, and SmO2 over all subjects comparing
two different datasets as described in the ﬁrst column. The comparison of Humon Beta at two locations on the muscle is performed on seven
subjects, which had two Humon devices on the same muscle.
Table 3. SmO2 feature comparison.
SmO2 Drop (%)
Time to 50% Recovery (sec)
SmO2 Overshoot (%)
Humon
11.9  5.9
123.4  39.7
8.2  4.9
MetaOx
11.6  5.8
100.8  42.4
6.9  3.7
P value
0.87
0.18
0.60
The SmO2% drop throughout the incremental test (ﬁrst column); The average time to reach 50% of the recovery (middle column), and the
average SmO2% overshoot (last column) are reported for the two systems. The p-values indicate there are not signiﬁcant differences between
the measured parameters with the two devices.
ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
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P. Farzam et al.
A New Wearable NIRS to Estimate Lactate Threshold
Inﬂuence of superﬁcial layers on the
measured SmO2
Using a silicone tissue mimicking phantom with known
optical properties, we veriﬁed that neutral density (ND)
ﬁlters minimally affect the recovered effective SmO2 as
measured by the Humon Beta and MetaOx (Fig. 6 plots
A and B). By placing neutral density ﬁlters of 0.1, 0.2,
0.5, and 0.7 optical densities over the phantom, the
FDNIRS light intensity measured by the MetaOx is atten-
uated near 100% (Fig. 6C). In the linear ﬁtting of inten-
sity, amplitude, and phase versus distance, the thin
absorbing layer affects the intercepts while the corre-
sponding slopes are minimally affected (Fig. 6D). As a
result, the recovered absorption and reduced scattering
coefﬁcients of the phantom are not impacted by the pres-
ence of the thin superﬁcial absorber. Therefore, the recov-
ered effective oxygen saturation (SmO2*) is not affected
as shown in Figure 6B, where the SmO2* changes with
respect to no ﬁlter are less than 2%. Similarly, Figure 6A
shows that the SmO2 estimated by the Humon Beta,
which also rely on multidistance algorithms, is not
affected by the presence of the ND ﬁlters.
Finally, to evaluate the effect of subcutaneous adipose
tissue on the estimated muscle hemoglobin parameters,
we compared
MetaOx
results at shorter and
larger
Figure 5. Two representative cases of Humon SmO2 with the estimated zones for (A) subject #2 and (B) subject #1. The measured blood
lactate concentration is plotted in the right y-axis (empty circles). The estimate 4 mmol/L blood lactate threshold is indicated by a red star. The
vertical lines indicate the different cycling power periods with the power level indicated on the top.
Table 4. Comparison of lactate threshold power estimated by
blood lactate concentration and Humon Beta proprietary algo-
rithm.
Subject #
Power
Difference (W)
Time Difference
(min:sec)
1
30
4:35
2
0
0:44
4
30
1:06
5
0
1:14
7
0
1:25
8
30
3:06
9
30
1:55
10
30
4:37
11
0
1:25
12
30
1:58
14
30
2:56
15
30
2:34
17
30
4:47
18
30
3:11
Absolute average difference
21.4  14.1
2:32  1:23
Difference between the threshold power found by the blood lac-
tate concentration measurements and by the Humon Beta algo-
rithm for the 14 male subjects, and the time difference between
the two. The positive values indicate power and time delays of the
Humon Beta with respect to the blood lactate. The last row
reports the absolute average values.
2018 | Vol. 6 | Iss. 7 | e13664
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ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
A New Wearable NIRS to Estimate Lactate Threshold
P. Farzam et al.
separations. Using larger separations, we consistently
obtained higher hemoglobin concentration. Across all
subjects, the difference in total hemoglobin concentration
is about 17%. Instead, the difference in SmO2 at the two
separations is small (2.8%  1.4%). In addition, we
found the SmO2 drop at the end of the exercise to be on
average 11.8  5.2% and 12.7  5.5%, using the shorter
and larger separations, respectively. By performing a Wil-
coxon Rank Sum test, these drops were not found to be
signiﬁcantly different (P > 0.05). This indicates that the
depth penetration is similar between the shorter and
longer separations used. To further assess the effect of the
subcutaneous adipose tissue on SmO2, we estimated the
correlation between the total SmO2 drop (using all
source–detector separations available in each system) and
the SCATT in all subjects. As shown in Figure 7, we con-
sistently
measured
smaller
SmO2
drops
for
thicker
Figure 6. Effect of a superﬁcial thin absorbing layer on measured optical parameters. Four different neutral density ﬁlters were positioned
between the optical probe and a silicone phantom to mimic a thin attenuating superﬁcial layer. Panels (A) and (B) report the difference
between the effective hemoglobin saturation (SmO2*, the SmO2 we would have estimated if there was blood in the phantom) measured by
Humon Beta and MetaOx with or without ﬁlter. The error bars represent the difference over ﬁve repetitions. Panel (C) shows the strong
attenuation of the detected light at 3.0 cm source–detector distance at a representative wavelength (690 nm) in the MetaOx system. Panel (D)
shows the corresponding amplitude and phase slope changes with the neutral density ﬁlters. Results are consistent at all wavelengths,
independent of the absorbance spectra of the ND ﬁlters. In fact, ND ﬁlters have constant absorption in the visible spectral range, but they
attenuate less in the near-infrared range. This difference in attenuation spectra does not affect the effective SmO2 calculated using both red
and near-infrared wavelengths, since at each wavelength the effect of the ND on the slopes is negligible.
Figure 7. Scatterplot of SmO2 drop during exercise versus
subcutaneous adipose tissue. With both devices, for thicker adipose
tissue we consistently measured smaller drops in SmO2.
ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
2018 | Vol. 6 | Iss. 7 | e13664
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P. Farzam et al.
A New Wearable NIRS to Estimate Lactate Threshold
subcutaneous adipose layers (with a correlation coefﬁcient
of 0.65).
Discussion
In this study, we recorded NIRS data from 17 athletes while
they were performing an incremental cycling test. The opti-
cally measured hemoglobin parameters (HbO, HbR, HbT,
and SmO2) responded to muscle physiological changes
during exercise. These results exhibit meaningful trends
that can provide insights to how muscles respond to exer-
tion. The average optical (absorption and reduced scatter-
ing coefﬁcients) properties are illustrated in Figure 2. The
measured values are consistent with values reported in the
literature using similar NIRS methods to measure skeletal
muscles (Yu et al. 2005; Gurley et al. 2012; Shang et al.
2012; Mesquita et al. 2013; Baker et al. 2017).
Comparison of Humon Beta and MetaOx
estimates of SmO2
During exercise, the body supplies a higher level of blood
ﬂow to the working muscles to provide oxygen (Yu et al.
2005; Joyner and Casey 2015). In the initial phase of
cycling, when the athletes were working at a lower power,
there was an overcompensation of blood, thus SmO2
slightly increased (Fig. 3 and Fig. 5). As the subjects
approached their lactate threshold power, the SmO2
decreased, indicating that the oxygen consumption in the
muscle had exceeded the oxygen supply. When the subjects
could no longer continue pedaling at high power, and
began the recovery phase, the SmO2 signiﬁcantly increased
due to the high blood supply and sudden decrease of mus-
cle oxygen consumption. The agreement between the esti-
mated SmO2 between the Humon and MetaOx system is
consistent across all phases of exercise and all subjects as
shown in Table 2 by the high correlation coefﬁcient (0.86).
Not only the trends, but also the absolute values on SmO2
are quite similar between the two systems, as shown by the
low average RMSE (3.4%). Figure 4 shows that the range of
variations of SmO2 measured with the Humon Beta system
is smaller than the range measured with the MetaOx. Using
the MetaOx subset model, we veriﬁed this difference is not
due to the ﬁxed scattering assumption. In fact, the R2
between the two MetaOx models is 0.87 and the range of
variation is actually larger by ﬁxing scattering than by cal-
culating it. Throughout the duration of the exercise, the
scattering coefﬁcients also changed minimally (on average
less than 10%). The difference between the Humon and
MetaOx estimated SmO2 is probably due to the different
consumption of the right and left leg muscles. Eighty-eight
percent of our subjects were right-footed and the MetaOx
probe was measuring the dominant leg, therefore this may
cause some discrepancies between measurements. Bilateral
differences have been found by Hesford et al. (2012), who
examined asymmetry between the SmO2 of the left and
right quadriceps in ice skaters, and found differences
depending on the muscle side that was being exerted more.
The data from Table 3, supports the similarities between
the features of the SmO2 curves measured on the 14 male
subjects by the MetaOx and Humon Beta. We investigated
the relationship between recovery time and maximum
blood lactate concentration, and similar to Chance et al.
(1992), we found these variables are uncorrelated. Chance
et al. (1992) did ﬁnd, however, that if individuals worked
at a fraction of their maximum power level, there was a
correlation between the recovery time and blood lactate
accumulation. We cannot verify this ﬁnding since in our
protocol we only measured recovery to maximum power
output.
Comparison of Humon Beta and MetaOx
estimates of hemoglobin concentration
While there was strong agreement between the SmO2 of
the two devices, we found larger differences in the hemo-
globin concentration values. This result is somehow
expected since the assumption of ﬁxed scattering heavily
affects the hemoglobin concentrations absolute values
(Fantini et al. 1999). Nevertheless, we observed that the
model error (ﬁxed scattering, no phase information, only
two wavelengths and only three separations vs. full
FDNIRS MetaOx data), is smaller than the effect of mus-
cle heterogeneity. As reported in Table 2, the RMSE
between Humon and MetaOx are similar to the RMSE
found comparing two Humon locations and double the
RMSE found comparing different MetaOx models. This
suggests muscle heterogeneity plays a larger role than dif-
ferences between CW and FDNIRS models. The hemoglo-
bin concentration spatial distribution throughout the
muscle has been previously reported (Hamaoka et al.
2011), and it needs to be considered, when assessing mus-
cle physiology with NIRS. Importantly, we found that
while hemoglobin content signiﬁcantly differs across loca-
tions, its temporal changes during the incremental cycling
exercise are uniform across measured parts of the working
muscle. In fact, in all our comparisons, we found good
correlation coefﬁcients for HbO, HbR, and HbT.
Determination of the threshold power
On average SmO2 values at exercise onset in the 14 male
subjects
were
67.8  4.0%
for
the
MetaOx
and
65.6  4.9% for the Humon Beta. SmO2 increased during
the initial power increments and at the lactate threshold
has decreased 6.2  4.6% and 6.3  3.7% from the
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ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
A New Wearable NIRS to Estimate Lactate Threshold
P. Farzam et al.
maximum SmO2, measured by the MetaOx and Humon
Beta, respectively. Because of large variability across sub-
jects, we could not ﬁnd an SmO2 threshold value that could
work for all subjects to estimate lactate threshold power
(the best attempt found an average power difference of
40 W and a delay of 5 minutes). This is in agreement with
previous NIRS works that show how the use of more com-
plex analysis techniques of SmO2 in addition to HbR,
HbO, and HbT are needed to better assess lactate threshold
power from NIRS (Belardinelli et al. 1995; Grassi et al.
1999; Wang et al. 2006; van der Zwaard et al. 2016). The
drop in SmO2 was larger when considering the maximum
SmO2 measured during the low power intervals as the ref-
erence value. SmO2 at rest, before starting pedaling, was on
average 3.5% lower than during the ﬁrst power interval
(30 W). We believe that in addition to the increase of
blood ﬂow with exercise onset which increases SmO2, the
muscle contraction and skin tightening during the pedaling
play a role here, reducing the contribution of superﬁcial
adipose tissue to the measured parameters. For this reason,
to avoid this possible measurement artifact, in all our anal-
yses, we only considered differences while pedaling, not
with respect to rest.
The Humon Beta uses a combination of HbO, HbR,
HbT, and SmO2 changes to identify the lactate threshold.
We tested the agreement between the Humon estimated
threshold with the one identiﬁed by the lactate concentra-
tion measures and found a close agreement between the
two. The Humon Beta’s real-time threshold power on
average only differs 21.4 W and less than 3 min from the
invasive measure of blood lactate (Table 4). On the seven
subjects with two Humon Beta devices, we also veriﬁed
the time at which the threshold power was found was on
average less than one minute apart between the two loca-
tions. The threshold power algorithms are proprietary to
Dynometrics Inc. and are being reﬁned as more data is
collected in controlled exercise settings like this one.
Finally, testing the effects that a light attenuating thin
superﬁcial layer has on the NIRS data is crucial to ensure
dependable results across a wide range of individuals with
different melanin concentration in the skin. It is impor-
tant to note that ND ﬁlters have constant absorption in
the visible spectral range, but they attenuate less in the
near-infrared range. This difference in attenuation spectra
does not affect the effective SmO2 calculated using both
red and near-infrared wavelengths, since at each wave-
length the effect of the ND on the slopes is negligible.
Both the MetaOx and the Humon Beta recovered correct
effective SmO2 values across all the absorbing ﬁlters used.
The reason why the effect of the ﬁlter is negligible is that
both the MetaOx and the Humon Beta recover the optical
properties from the gradient over distance, not from the
absolute intensity values. This ensures conﬁdence that the
Humon Beta can measure SmO2 in people of all skin col-
ors (Franceschini et al. 1998).
Limitations
It is important to address that when validating the
Humon Beta against the MetaOx, the NIRS data was
obtained on different legs of the subjects. While cycling
typically engages both legs uniformly, more differences
may arise in the data because the two systems were not
measuring the exact same location.
For the lactate power analysis, we had to exclude the
female subjects, since we realized this protocol is not well
suited for females. The power increments were too large,
which resulted in the lactate threshold power and end of
the exercise to be too close in time. We suggest smaller
power increments for each step to be used when measur-
ing female subjects who have smaller quadriceps com-
pared to males.
The subcutaneous adipose layer inﬂuences quantiﬁca-
tion of hemoglobin parameters, especially at shorter
source–detector separations. We veriﬁed that using larger
(2.5–3.0 cm) source–detector separations relative to the
shorter (1.5–2.0 cm) we obtain an average of ~17%
higher hemoglobin concentration over the exercise dura-
tion. This is expected because of the larger penetration
depth at longer separations and because of the larger
hemoglobin concentration of muscle with respect to adi-
pose tissue. As a result, in the presence of a subcutaneous
adipose tissue layer, we measure higher hemoglobin con-
centrations at larger separations. Since fat and muscle
have similar oxygenation at rest, the difference in SmO2
at the two separations is small (2.8%  1.4%). We veri-
ﬁed that during exercise, the difference in SmO2 at the
two sets of distances is constant and did not have statisti-
cally signiﬁcant differences in the SmO2 drops by the end
of exercise. Nevertheless, at both sets of distances, the
presence of a subcutaneous adipose tissue layer affects the
measured SmO2 as shown by the correlation between the
SmO2 drop and the SCATT in Figure 7. For thicker
SCATT layers, there are smaller drops in SmO2 since the
volume measured includes less muscle and more fat. For
the speciﬁc application in athletes and ﬁt individuals, the
SCATT is low, reducing the contamination by the adipose
tissue. While we only measured the SCATT over the rec-
tus femoris of the right leg, we also assumed that the
SCATT is symmetric and that this layer reported in Fig-
ure 7 affect similarly the MetaOx and Humon Beta data.
In this study, we chose to monitor the rectus femoris
rather than the vastus lateralis muscle (which is the com-
mon choice in other work examining NIRS measurements
in sports (Perrey and Ferrari 2018) to minimize optical
ﬁber movement throughout the cycling exercise. Further
ª 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of
The Physiological Society and the American Physiological Society.
2018 | Vol. 6 | Iss. 7 | e13664
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P. Farzam et al.
A New Wearable NIRS to Estimate Lactate Threshold
work needs to be done to examine the differences in
SmO2 measured in multiple muscle groups simultane-
ously, using several Humon Beta devices to help under-
stand what muscle group is the optimal choice for certain
types of activities. It is also crucial to determine if the
identiﬁcation of an athletes’ threshold by NIRS is depen-
dent on the exact muscle chosen from the many working
muscles during a sport.
When comparing the NIRS measurements against the
lactate threshold, it is relevant to mention that multiple
methods exist to ﬁnd the lactate threshold (Faude et al.
2009). By choosing a different threshold identiﬁcation tech-
nique (besides 4 mmol/L), a new threshold power may be
identiﬁed, which may alter the values in Table 4. We chose
to determine the lactate threshold as 4 mmol/L because of
the widespread use and established studies that support this
method (Heck et al. 1985; Faude et al. 2009).
Conclusion
In summary, this study validates the performance of a low-
cost, wireless, wearable NIRS device against an advanced
benchtop device. During the incremental cycling test, the
wearable device provides similar results to the more expen-
sive FDNIRS technology. We veriﬁed that the assumptions
and simpliﬁcations of this CWNIRS system minimally
impact the SmO2 quantiﬁcation and the recovery of
changes in the hemoglobin concentration trends. The main
deviations are accounted for by muscle heterogeneities.
While skin color does not affect the results, the main limi-
tation, common to all CW and FDNIRS systems, is the
reduced sensitivity to muscles in the presence of subcuta-
neous adipose tissue. Targeting athletes, who tend to be ﬁt
individuals with thin adipose layers, provides a larger drop
in SmO2 readings than what can be achieved on people
with thicker adipose thicker layers. Finally, we demon-
strated the Humon Beta shows a good accuracy in predict-
ing the lactate threshold level. Thanks to the low cost, small
size, and low sensitivity to motion, the Humon Beta wear-
able and its accompanying smartphone application can
provide useful information about exercising muscle physi-
ology and help athletes optimize their training.
Acknowledgments
We thank Dr. Pamela Anderson and Dr. Daniel Wiese from
Dynometrics, Inc. for the scientiﬁc interaction and help
with the measurements and analysis of the Humon data.
Conﬂict of Interest
Dr. Franceschini has patents on the FDNIRS technology
used in this paper.
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A New Wearable NIRS to Estimate Lactate Threshold
P. Farzam et al.

Validation of a novel wearable, wireless technology to estimate oxygen levels and lactate threshold power in the exercising muscle.
Farzam, Parisa,Starkweather, Zack,Franceschini, Maria A