Biosensors And Bioelectronics Presentation by Sijung Hu

Opto-physiological modelling to drive an effective
physiological monitoring:
from contact to noncontact, from point to imaging
Sijung Hu
Leader of Photonics Engineering and Health Technology Research Group
Wolfson School of Mechanical, Electrical and Manufacturing Engineering,
Loughborough University, UK
12 July, 2021

Content of presentation
• Introductions
• Limitations with photoplethysmography (PPG)
• A new model for light trans-illuminating tissue (LTT)
• Opto-Physiological Monitoring (OPM)
• Commercial opportunities for OPM

Wolfson School
of Mechanical, Electrical, and
Manufacturing Engineering

The LU Photonics Engineering & Health Technology conducts research into:
1. The use of light for sensing and characterising dynamic systems of industrial and biological origin.
2. The research is mainly specialised in Photonics based Biomedical Engineering, i.e., photoplethysmography,
Tissue optics engineering simulation, light scattering etc., and has the following generic expertise:
Optics/Tissue Optics, (µ)electronics , software design and systems integration
at component and systems levels, combined with a team approach to problem solving, allows for a clear and
rapid progression from fundamental research ideas to industrial prototypes.
The majority of the Group's projects are pursued to the level of engineering implementations and further
commercial exploitation.
3. Five academic members including associated members, one visiting clinical professor, two visiting academics,
23 graduated PhDs (since 2006) and eight on-going PhDs arranging from opto-physiological monitoring (point
and imaging) to in vitro diagnosis (IVD) optical detector system for point-of-care testing (POC) and dynamic
breathy pressure sensing.

Remarkable achievements
2008: “Opto-physiological monitoring” a milestone of optoelectronic research worldwide
www.pages.drexel.edu/~kmg462/currentresearch.html
2010: Multiplexed POCT Instrument in Future Pavilion, World Expo 2010, Shanghai.
2012: LU non-contact vital sign monitoring, as highlighted in NASA/TM-2011-216145
2014: “Opto-Physiological Sensor and Method of Design”, PATENT No. GB1511448.1.,
CareLight: www.lboro.ac.uk/carelight
2015: New Generation of optoelectronic sensor for vital human sign monitoring
http://atlasofscience.org/tag/carelight/
2017: A new temporal dynamic correlation between physiological changes and
spontaneous expressions (2017, Nature_ Scientific Reports, DOI:10.1038/s41598-
017-07122-x).
2019: A breath activated dynamic air pressure detection system (DAPDS) created in LU.

Limitations with
Photoplethysmography
(PPG)

 The use of oversimplified PPG models to describe and implement the
technology has limited its applicability;
 The principle of PPG is typically described as a blood-filled cuvette
based on the Beer-Lambert law;
 No scattering effects (µs, g) and the light sources are assumed to be
monochromatic.
PPG is an optically obtained plethysmogram, a volumetric measurement of an organ, and
illuminates the skin and measures changes in light absorption.

 The amount of light transmitted from the
skin and received by the photodetector
 Two modes of PPG probe
 Factors (blood volume, vessel wall
movement, orientation of red blood cells
(RBC) and wavelength of optical radiation)
Transmitted mode Reflection mode
R. Anderson, John Parrish, “The Optics of Human Skin”, J
Invest Dermatol, 77(1), 13-19, 1981

Beer-Lambert Model based PPG
where I0 is the source intensity,
I is the transmitted intensity,
µeff is the effective absorbance and
r is the optical path length.
Usually µeff is formed by a static µtissue and a dynamic µblood components:
The µblood takes into account the oxygenation of the arterial blood:
( )
( )
0 exp eff
I I r
µ λ
= −
S. Feng, F. Zeng, and B. Chance, "Photon migration in the presence of a single defect: a perturbation analysis," App. Opt., 34: 3826-3837 1995.

PPG devices are in widespread use
Pulse oximetry: $1,913.2 m by 2024 at a
compound annual growth (CAGR) of >6%
Global Pulse Oximeter Market, accessed on
10th September 2020
Wearable Medical Devices: $62.44 b by 2027 at a CAGR of 25.8%
Wearable Medical Devices Market Size, accessed on 10th
September 2020

PPG devices are in widespread use
…in spite of well known limitations!
• Interference from motion artifacts
– Pulse oximetry (SpO2%) users cannot move around
• Poor analytical performance
– Smartwatch heart rate data not clinically acceptable
• Lack of calibration with reference standards
– Uses largely confined to consumer applications

If the internal temperature drops outside of the
normal range, your body will take steps to adjust it.
• Thermogenesis
• Vasoconstriction
• Hormonal thermogenesis
Thermoregulation:
These mechanisms include:
Internal body heating
26° 29° 32°
Understanding Thermoregulation in humans

Poor circulation when Cold
Better circulation when warm
Venule
Arteriole
Venule
Arteriole
Skin Surface
Good signal
Poor signal
Skin Surface
Effective capture of light trans-illuminating tissue

Shivering
Sweating
Set point
Vasoconstriction
Vasodilation
Time
Body
temperature
(C°)
37
37.5
36.5

Heat out Reduces heat loss
Cold conditions
Warm conditions
• Vasoconstriction in cold conditions:
shunt vessel relaxes so less blood
flows to the surface capillaries
• Vasodilatation in warm conditions:
shunt vessels constricts so more blood
goes to the surface
The signal may be affected by the thermoregulation!

A new model for light trans-
illuminating tissue (LTT)

Opto-physiological interaction
Biological tissue is considered as a set of optical media, study how light interacts
within biological tissue, where the optical properties of the latter reflect the
mechanical, physical and biochemical functions of the living organism.

Tissue Optics
Light interaction and propagation in biological tissue, are investigated. The
light propagation in turbid biological media is jointly governed by the
absorption (µa), anisotropic scattering properties (µs, µs‘, g) as provided by
radiative transfer equation (RTE)
The knowledge of tissue optical properties has found numerous applications,
i.e. cancer diagnostics and therapy.

Tissue Optics.
Radiative Transport Theorem
s
t
4
( ,
( , ( , ' ( , ' d '
4
I
I I f
s π
µ
µ
π
∂
=
− + Ω
∂ ∫
r s)
r s) r s ) s s )
where is the radiance at a position r in direction s, and µt = µs + µa,
is the phase function that describes anisotropic scattering and
dΩ’ is the solid angle.
The diffusion approximation P1 assumes isotropic scattering through all media by
using the transport scattering coefficient .
' (1 )
s s g
µ µ
= −

Opto-physiological model for light trans-
illuminating finger tissue
`
PREPARE 3D
MODEL
3D STUDIO MAX
TRACE RAYS
OPTICAD
Determine mean path lengths with
respect to detector position
RAYDETECT
Ray segment data
SIMULATION
MODEL PREPARATION Finger Dimensions
3D MODEL
Corrected Model
Rays (raw)
POST-PROCESSING
Rescale model according to empirical
validation subject
STLSCALE
Scaled Model
Detector parameters
Perform measurements used in
processing and compile data
STLCOMPILE
Model data
INTERFACING
Group, segment and measure rays
Map ray exit points on 2D surface
RAYPROCESS
Rays (processed)
Optical parameters
Optical
Parameters
Iterate abs. and dynamic coeffs. to
maximise correlation btw sim. & val.
IntensityMaps
Coefficients & correlation
Iteration parameters
OUTPUT
EMPIRICAL VALIDATION
AC & DC distributions
Assumptions
V. Azorin Peris, S Hu, P. Smith, “A Monte Carlo Platform for the Optical Modeling of Pulse. Oximetry”,
Proc. SPIE, 6446, 64460T (2007)
Output
RAYTESTROT
Determines the intersection
of rays with interfaces.
FLATS2SURF
Converts 2D maps of exit points into
intensity distribution surfaces.
RAYENDFLAT
Determines the points at which rays
exit the tissue bed and maps these
onto a flat surface (x in mm, y in deg)
ORIGINS
Determines the central
axis of the finger
Procedure

`
PREPARE 3D
MODEL
3D STUDIO MAX
TRACE RAYS
OPTICAD
Determine mean path lengths with
respect to detector position
RAYDETECT
Ray segment data
SIMULATION
MODEL PREPARATION Finger Dimensions
3D MODEL
Corrected Model
Rays (raw)
POST-PROCESSING
Rescale model according to empirical
validation subject
STLSCALE
Scaled Model
Detector parameters
Perform measurements used in
processing and compile data
STLCOMPILE
Model data
INTERFACING
Group, segment and measure rays
Map ray exit points on 2D surface
RAYPROCESS
Rays (processed)
Optical parameters
Optical
Parameters
Iterate abs. and dynamic coeffs. to
maximise correlation btw sim. & val.
IntensityMaps
Coefficients & correlation
Iteration parameters
OUTPUT
EMPIRICAL VALIDATION
AC & DC distributions
Opto-Physiological Modelling

Opto-Physiological Modelling: Point Measurement
s
t
4
( ,
( , ( , ' ( , ' d '
4
I
I I f
s π
µ
µ
π
∂
=
− + Ω
∂ ∫
r s)
r s) r s ) s s )
Accuracy
Applicability
Experimental
Setup
Opto-
Physiological
Modelling
Radiative Transport Theory
Beer-Lambert
( ) ( )
( ) ( )
2
2
1 1
2 2
(1 )
(1 )
HbO Hb
HbO Hb
S S
R
S S
µ λ µ λ
µ λ µ λ
+ −
=
+ −
( )
( )
( )
( )
1 1 1
1
1 2
2 2 2
1
exp 1 ( , ) ( , ) ( , , , )
( , )
exp 1 ( , ) ( , ) ( , , , )
n
a det
i
n
a det
i
i i L i x
R
i i L i x
σ λ µ λ λ θ
λ λ
σ λ µ λ λ θ
=
=
 
+
 
 
=
 
+
 
 
∑
∑
Modified Beer-Lambert
[ )
( )
2
1
1
( ) max ( ), , 0,
ACpeak
i DC
I
x i x
I
λ θ π
=
 
Θ =  
 
∑
[ )
( )
2
2
1
( ) max ( ), , ,2
ACpeak
i DC
I
x i x
I
λ θ π π
=
 
Θ =  
 
∑
Optimum sensor placement
( ) ( )
( )
0
1
( ) exp ( ) ( ) 1 ( ) , ( ), ( ), ( )
n
AC a det
i
I t I i t i L i x t t s t
σ ρ ω µ θ
=
 
= −
 
 
∑
Sensor motion artefact

Experimental Validation Setup and Outcomes
Finger
Rotating Platform
LED 1 LED 2
LED Driver
CAM
Trigger
PC
Framegrabber
Ref: Azorin-Peris, V., Hu, S.*, Smith, P. R.,“A Monte Carlo Platform for the Optical Modelling of Pulse Oximetry”,
Proc BiOS, SPIE 2007, 6446,64460T, Photonics West, San Jose, USA, 2007

Opto-Physiological Model of light trans-illuminating general
tissue : Imaging measurement:

Revised Beer-Lambert Law
0 exp( )
a
I I MPL
µ
= × − ×
0
I
I
a
µ 1
mm−
MPL mm
intensity of incident light.
intensity of transmitted light.
the wavelength-dependent absorption coefficient with units of
the mean path length with unit of
( ) ( , )
s
MPL S G l g
µ
= + ×
The mean path length MPL is defined as follows:
Where
S a factor that accounts for the position and size of light source and detector
G a factor that accounts for a specific tissue model including the geometry and the
layered-interaction.
( , )
s
l g
µ the path length modulated by the wavelength-dependent scattering coefficient
with units of 1
mm−
and the anisotropy factor g
Opto-Physiological Model for Imaging

Time-Variant blood perfusion
( )
0 , ,
exp[ ( ) ]
a static a blood
I I d r t
µ µ
= × − × + ×
,
a static
µ the wavelength-dependent absorption coefficient of the static component such
as baseline arterial and venous blood, tissue, bone etc. with units of 1
mm−
,
a blood
µ the wavelength-dependent absorption coefficient of the arterial blood with
units of 1
mm−
d the static component of MPL with units of mm
( )
r t the time function of the pulsatile component of MPL during the arterial blood
variation
,
exp( ( ))
static a blood
I I r t
µ
= × − × Where 0 ,
exp( )
static a static
I I d
µ
= × − ×
0 ,
exp( )
static a static
DC I I d
µ
= = × − × with the amplitude equal to the value of this signal. static
dc DC I
= =
, ( )
static a blood
AC I r t
µ
= × × With the amplitude ,
diastole systole static a blood
ac I I I r
µ
= − = × ×

Phantom to valid opto-physiological mode
Construction of phantoms with different absorption and scattering coefficients
Ref: J Zheng, “OPTO-PHYSIOLOGICAL MODELLING OF IMAGING PHOTOPLETHYSMOGRAPHY”, PhD thesis,
2010, Loughborough University, UK

y = 1.7184x + 0.5123
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1
Absorption coefficient (mm-1)
Absorption
(a.u)
Beer-Lambert law
Opto-physiological
model
Experimental and theoretical absorption on a two-layered phantom with a lower scattering top
layer
For the top layer, the absorption and scattering coefficients were set to be μs,t=1.00mm-1, μa,t=0.0263 mm-1, respectively. The square blocks
represent the absorption in the experiment corresponding to the different absorption coefficients of bottom layer μa,t: 0, 0.0122, 0.02625, 0.0455,
0.0612 and 0.0789. Ref: Hu, S.*, Azorin Peris V., Zheng J., “A study of opto-physiological modelling to quantify tissue absorbance in imaging
photoplethysmography”, Proc. IEEE EMBC 32, 2010, 1: 5776 – 5779.

Opto-Physiological Modelling: Imaging Measurement
RCLED arrangement
of the dual-wavelength
Ringlight with the lens
in the middle
650nm
870nm
RCLED arrangement
of the dual-wavelength
Ringlight with the lens
in the middle
650nm
870nm
650nm
870nm
Trigger
Timing signals
650nm
870nm
Trigger
Timing signals
650nm
870nm
Trigger
Timing signals
Digital CCD camera

The Fourier spectrum for PPG signals from the conventional contact PPG imaging
system and imaging PPG system at 650nm and 870nm from the face
Human face
Raw signal
Filtered signals
(PPG)
Region of interest
Results: face

Opto-Physiological Monitoring
(OPM)

Applications of OPM
• Contact monitoring (wearables)
• Multiplexed contact sensor (advanced wearable, Carelight)
• Imaging OPM
• 3D mapping OPM

Point Measurement:
Venox: World 1st non-invasive venous oximetry
- A non-invasive measurement mechanism of venous oxygen saturation (SvO2).
- Providing enhanced monitoring of essential cardio-vascular function.
- Indicating a correlation of oxygen saturation changes between Venox and the bypass machine.
Ratio-of-Ratios
a
arterial
R
V
A
V
A
t
I
t
I
t
I
t
I
=
−
−
=
∆
∆
)
(
)
(
)
(
)
(
)
,
(
)
,
(
)
,
(
)
,
(
2
2
1
1
2
2
1
1
λ
ε
λ
λ
ε
λ
λ
λ
λ
λ
v
venous
R
V
A
V
t
I
t
I
t
I
t
I
=
Α
−
−
=
∆
∆
)
(
)
(
)
(
)
(
)
,
(
)
,
(
)
,
(
)
,
(
2
2
1
1
2
2
1
1
λ
δ
λ
λ
δ
λ
λ
λ
λ
λ
Venox
Ref: Venous pulse oximetry, WO2003063697A1, granted in 2008

LU Multiplexed Optoelectronic Sensor (Carelight,
www.Lboro.ac.uk/carelight)
• Novel sensor design
– Redundant illumination sources at multiple wavelengths
– Additional sensor options e.g. accelerometer, temperature
• Multiple versions built in house
– Tested on colleagues v’s gold standards
Granted Patent:
GB 2519335, WO2015/056007
PCT/GB2014/053095
Pulsatile waveforms (PPG signals) with different wavelength illuminations and contact points

Tests against Gold standards
Heart Rate (the correlation r >0.99 against 5
Lead ECG, Mortara-H3Plus-AAB, 20
subjects with various ethnical groups* )
Oxygen Saturation SpO2% against standard
pulse oximeter (ContecTM PM60A), 20 subjects
with various ethnical groups*)
*Including dark skin type subjects
Algorithm: Bandpass filter:
0.5 – 5Hz
Algorithm: Bandpass filter: 0.5 – 5Hz,
Ratio of Ratios;
Ratio=AC(red)/DC(red)/AC(ir)/DC(ir)

List of Carelight work
• “Illumination Adaptation in a Multi-Wavelength Opto-Electronic Patch Sensor”, Sensors 2020, 20(17), 4734; “An
Optimization Study of Estimating Blood Pressure Models Based on Pulse Arrival Time for Continuous Monitoring”,
J Health Eng. 2020, doi: 10.1155/2020/1078251.
• “Oxygen Saturation Measurements from Green and Orange Illuminations of Multi-Wavelength Optoelectronic
Patch Sensors”, Sensors 2018, 19(1), 118; doi.org/10.3390/s19010118
• “A Multi-wavelength Opto-Electronic Patch Sensor to Effectively Detect Physiological Changes Against Skin
Pigments”, Biosensors, 7(2), 22, (2017), doi:10.3390/bios7020022
• “A Multi-Channel Opto-Electronic Sensor to Accurately Monitor Heart Rate against Motion Artefact during
Exercise”, Sensors, 15, 25681-25702 (2015); doi:10.3390/s151025681
• “A Comparison Study of Physiological Monitoring with a Wearable Opto-Electronic Patch Sensor (OEPS) for
Motion Reduction”, Biosensors, 5, 288-307 (2015); doi:10.3390/bios5020288
• “Opto-physiological modeling applied to photoplethysmographic cardiovascular assessment”, Invited Review
Paper, J Healthcare Eng. 4 (4), 505-528 (2013).
• “Non-contact Reflection Photoplethysmography towards Effective Human Physiological Monitoring”, J Med. Biol.
Eng. Vol.30 Iss 30 (2010): 161-167
Much more ……………

Blood Flow vs Perfusion Index
Perfusion Index = AC/DC

Real time signal processing to obtain heart rate (HR) and respiration rate
(RR) against standard methods
HR calculation results on two subjects chosen LU-Db data sets with Carelight
sensor. (a) The results of subject F03 in 1st session. (b) The results of subject
M08 in 2nd session

RR calculation results on two subjects chosen LU-Db data sets. (a) The
results of subject F03 in Cycling; (b) The results of subject M08 in Treadmill.

Non-contact reflection OPM monitoring
The purpose is to noncontact measure blood volume change and to overcome the problems related to
contact sensors as requested for a variety of surgical and medical applications, e.g. human metrology,
assessment in sites of tissue damage, etc.
I
I
I I
I
I I
I
P
23 mm
Infrared light sources
Photodetector
Two research prototypes
Two research prototypes
Photodetector
Light Source
Probing
area
Illuminated
area
dPD
dLS
θLS
Ref: Shi, P., Hu, S.* et al, “Non-contact Reflection Photoplethysmography towards Effective Human Physiological
Monitoring”, J Med. Biol. Eng. Vol.30 Iss 30 (2010): 161-167

Imaging measurements: 3D Opto-physiological imagine

OPM driven 3D imaging PPG
OPM allows to monitor larger area and different depths in tissue, which can give a complete 3-D description of
tissue, so as to improve the ability to probe biologic interactions dynamically and to study disease over time.
The mean values in a region of interest
against time plotted in time domain
The mean AC amplitude of individual regions
from 660nm and 880nm LEDs in a 3-D format
Ref: Zheng, J., Hu, S.*, et al “Remote simultaneous dual wavelength imaging photoplethysmography: a further step towards 3-D
mapping of skin blood microcirculation”, Appl. Phys. Lett. 0003-6951, Proc. of SPIE Vol. 6850 68500S-1, Photonics West, San Jose,
USA, 2008.

3-D Visual Microcirculation Map
OPM driven 3D iPPG
OxiMap: Vascular Imaging was filed as a patent with PCT/GB2008/003042.

OPM for
wearables
1. Flexible OPM sensor
2. Smart garment fabric with conductive tracks and LEDs
3. Smart Textile
4. Fatigue alarm
Ref: D. Iakovlev., S, Hu., “Smart Garment Fabrics to Enable Non-Contact Opto-Physiological Monitoring”, Biosensors (Basel).
2018 Jun; 8(2): 33. doi: 10.3390/bios8020033
1 2 3 4

Commercial opportunities
for OPM

Smart Wearable
Smart Jewellery
Wrist-worn
Head bands
e-textile
Smart Mask

Smart Device for medical, more
Sleep Monitor Pulse Oximeter
Brain computer interface
Bio-Security Automation

 Analogue Front End.
 Digital Processing.
 Wireless Communication.
We are seeking for partners for commercialisation to create small scale
prototype mobile electronics that are suitable for wearing with the OPM sensor

Thank you for your attention
Any questions?

Biosensors And Bioelectronics Presentation by Sijung Hu

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