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Facial Gesture Measurement Using Optical Muscle Sensing System

Authors:
  • Institute of Vocational Education Northeastern Region 2; Sakonnakhon
http://www.nanobe.org
Nano Biomed Eng
2015; 7(4): 169-179. doi: 10.5101/nbe.v7i4.p169-179.
Research Article
169
Nano Biomed Eng 2015, Vol. 7, Issue 4
Facial Gesture Measurement Using Optical Muscle
Sensing System
Abstract
A PANDA ring resonator circuit has been applied to the measurement of muscle actions, measuring
signals created by facial muscle contractions. A system, which is called the Optical Muscle Sensing
System, was developed which uses sensors to measure the mechanism of facial muscle contractions
and the strength of contraction and degrees of perturbation of the facial muscles that are used directly
for each facial gesture. The signal data was obtained from the simulation of the facial gestures and
this data was applied in the classication of the facial gesture signals of each particular gesture. Facial
gestures include blinking, smiling, grimacing and various other contortions of the face which may
imply emotions and are part of normal human communication. Understanding of these mechanisms
will be useful and applicable to facial rehabilitation services.
Keywords: Facial gesture measurement; Optical muscle sensing system; Optical sensor Facial
gesture signals; PANDA ring resonator; Facial gesture recognition
Kriengsak Yothapakdee1, Preecha P. Yupapin3, Kreangsak Tamee1,2
1Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Phitsanulok 65000,
Thailand
2Research Center for Academic Excellence in Nonlinear Analysis and Optimization, Faculty of Science, Naresuan University,
Phitsanulok 65000, Thailand
3Interdisciplinary Research Center, Faculty of Science and Technology, KasemBundit University, Bangkok 10250, Thailand
Corresponding author: E-mail: kreangsakt@nu.ac.th
Received: Oct. 1, 2015; Accepted: Dec. 30, 2015; Published: Jan. 22, 2016.
Citation: Kriengsak Yothapakdee, Preecha P. Yupapin and Kreangsak Tamee. Facial Gesture Measurement Using Optical Muscle Sensing System. Nano
Biomed. Eng. 2015, 7(4), 169-179.
DOI: 10.5101/nbe.v7i4.p169-179.
Introduction
Human gestures or body language is recognized as
playing an important role in human communication
which complements verbal communication. Gesturing
and body posture, arm and hand gestures, face
movement and pose, facial gestures and eye movement
and gaze are all important communication patterns.
Such body language provides a powerful source
of communicative information in a human gesture
recognition system [1]. The objective of developing
and improving these technologies is to provide various
powerful applications to assist the disabled and
elderly in normal daily activities and in rehabilitation
and support services. Applications that have been
developed have been for use in Human-Computer
Interaction (HCI) or Human-Machine Interaction
(HMI), and are generally known as Sensory Substitute
Systems.
Scientific researchers in the field of rehabilitation
engineering have attempted to develop various devices
for helping patients, the disabled and the elderly [2-4]
and there has been considerable success in achieving
improvements to the quality of life for these people
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by the application and use of these various devices.
However, these devices have usually been based on the
physical movement of the body, particularly the head,
arms, fingers, legs and feet, and these devices often
have use and usefulness to only certain user groups.
For example, most of these devices are not useful or
appropriate to people with paralysis of part or all of the
body.
Nonetheless, certain levels of muscle weakness and
physical movement limitations have been improved
and physical capabilities developed through the use
of devices that the measured and analyzed electrical
signals acquired from the body, called Bio-signals
[5, 6]. These signals originate at the molecular
level, cell level or at the systemic or organ level
[7]. Examples of well-known and detectable bio-
signals include electromyograms (EMG) which
emanate from the electrical activity from the muscles.
Electro-oculograms (EOG) are signals detected from
eye movements as are electroretinograms (ERG)
from the eye. Electrical signals from heart muscle
activity; the electrocardiogram (ECG) as well as the
electroneurogram (ENG); field potentials from local
regions in the brain are all examples of detectable
signals from muscles and organs in the body. These
bio-signals have been identified and are detectable
and are widely used in various control applications
such as controlling a motorized wheelchair by EOG
signals [8, 9], a glove which is a force-audio sensory
substitution system for diabetic patients [10], and a
visual substitution system for blind people [11, 12].
Particularly, Facial Gesture Recognition (FGR) offers
a challenges and is certainly of interest. Such as system
uses human gestures in application of robotic control
and computer vision, generally known as Human-
Robot Interaction (HRI) [13-15]. These systems have
been investigated with a focus on the advantages of
using human gesture recognition in many application
areas [16-19]. Unfortunately, these devices still
problems with over-heating after continuous use for
a long time with continuous energy consumption.
Heating within the circuitry of the device is also usual,
as a chemical reaction, oxidization and corrosion.
Therefore, optical devices are the only alternative.
Optical devices can be used to replace mechanical
devices or parts of such devices, or increase the
efficiency and effectiveness of those devices and
applications, now, and certainly in the future. The
advantages of optical devices include being immune to
radio frequency interference (RFI) and electromagnetic
interference (EMI), being very small, passive and with
low power consumption, having excellent performance
with high sensitivity and wide bandwidth and being
non-contaminating of their environment. Finally, they
are not subject to corrosion [20]. Optical sensors based
on a ring resonator have been used in robotic control
systems, security applications and in monitoring and
detection applications in industry, such as production
line inspections and quality control.
One advanced application is their use in nano-
scale sensors, which is a specific model of PANDA
ring resonator type reported by P.P. Yupapin and et
al [21]. They have been developed for and applied
to a wide variety of applications; distributed sensors
[22], molecular sensors [23], gas sensors [24] and
force sensing application [25]. Recently, Yupapin and
Sarapat have shown the advanced optical sensing
system that uses the whispering gallery modes (WGMs)
of light within a micro-scale modified add-drop filter
circuit for various sensing applications [26]. In our
previous research [27-28] we developed a conceptual
framework and device model of a muscle measurement
system using optical devices which measure muscle
contractions. As well, the results from our previous
research can be used for pattern recognition. The
PANDA ring resonator circuit still has obstacles
associated with limitations in materials, sophisticated
production technology and relatively high production
costs. These factors have been obstacles to the
fabrication of PANDA ring circuits which are currently
not being produced.
Thus, we have proposed an approach which
uses simulation of the processes of facial gesture
measurement using what we term the Optical Muscle
Sensing System. In this system, in order to have
consistent facial gesture signal data which is as close
as possible to measurements from a real situation,
we generate a signal based on the theory of muscle
contraction and optical sensing mechanisms. The
simulations were conducted by using the MATLAB
and Opti-wave software programs. Although our
performance is a simulated work, we are condent that
the same device parameters and methodologies can be
applied to real devices, which will enable them to be
fabricated and implemented in the near future. This is
an important advance towards making these optical
devices useful for nano-scale sensing systems, human
computer interaction, human-robot interaction and in
disability applications.
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Muscular Contraction and Optical
Sensing Mechanism
Almost all of the procedures or mechanisms that
affect the process of movement of the organs in the
mammalian body will be based on the muscular
contraction mechanisms. Typically, the muscles can
be categorized into three types; skeletal, cardiac, and
smooth. The skeletal muscles are voluntary muscles
that allow us to control the gestures and the movements
of our body. The skeletal muscles are composed
of bundles of muscle fibers or cells, each of which
are composed of sub-units called myofibrils, which
are composed of many myofibrils, organized into
contractile units of skeletal muscles called sarcomeres.
These sarcomeres are the smallest functional unit
of the muscle fiber and are responsible for muscle
contraction. In the past, the main mechanism for
muscle contraction has been described as principles of
interaction between the two sets of protein myobrils
within the sarcomere known as the sliding filament
theory [29], which is pulling the Z-lines (bundles
of fibrous cells responsible for separate and link
sarcomeres within a skeletal muscle) towards the
center of the sarcomere. Such reactions are the cause
of the shortened muscle bers as shown in Fig. 1.
The cross-bridge theory [30-32] is now recognized
as the paradigm of a suitable approach to describing
and simulating the mechanism of muscle contraction.
According to this theory, the muscle contraction occurs
through the interaction of myobrils which is the main
component inside the sarcomere. The simple working
model of the cross-bridge cycle could explain how
the myosin lament head moves in order to attach the
actin filament, thereby forming a sort of cross bridge
between the two laments as shown in Fig. 1.
The force of muscle contraction can be detected and
measured based on the number of myosin and actin
cross bridges that are attached. This in turn is affected
by four factors [33]: (1) the number of muscle fibers
stimulated, (2) the relative size of the fibers, (3) the
frequency of stimulation and (4) the degree of muscle
stretch, which can be divided into three periods: (1)
Fig. 1 A cross-bridge areas within sarcomere, and the contractile actin and myosin laments.
Sarcomere
I band A band
Z line Cross-bridge areas Z line
I band
Actin filament Myosin filament
M line
I band A band I band
1 Relaxed
2 Contracting
3 Fully contraction
Fig. 2 The individual degree of muscle contraction.
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sarcomeres greatly shortened, (2) sarcomeres at resting
length, and (3) sarcomeres excessively stretched. Here,
we call these the relaxed state, the contracted state, and
the full contracted state respectively, as show in Fig. 2.
Fig. 3, from [34], shows the dependence of force
on sarcomere length. The shortening of the sarcomere
from (A) to (B) 3.65 μm to 2.25 μm results from the
relaxation of the muscle, (B) to (D) 2.24 μm to 1.67
μm is the contraction of muscles, and (D) to (E) 1.66
μm to 1.27 μm is full contracted of muscles. So, the
relationship between the sarcomere length and the
degree of muscle contraction can be described by a
shortening of the muscle.
The Optical Muscle Sensing System has been
devised and modified on the basis of a PANDA ring
resonator circuit. The schematic diagram of the system,
which consists of three micro-ring resonators, is shown
in Fig. 4(1). The first ring is arranged as a reference
unit (RL). The second ring RR is the sensing unit,
and the third ring is used to form interference signals
between the signals from the rst ring and the second
ring. The waveguide material and suitable parameter
congurations are well described in [21-25].
The explanation of the method performed within
the Optical Muscle Sensing System can be categorized
into two cases, the non-perturbed situation and the
perturbed situation. In the first case, the part of
the sensing unit (RR) non-perturbed by the muscle
contraction, can be simulated by introducing Gaussian
beams into the input port of the PANDA ring resonator
circuit. The output signal from both the reference unit
(EL) and the sensing unit (ER) can then be detected at
the drop port. The relationship between the peaks of
Fig. 3 The relationship between the shortening of the sarcomere
and the sarcomere length [34].
100
80
60
40
20
0
Tension (% of maximum)
1.0 1.5 2.0 3.0
Sarcomere length/μm
3.53.652.251.671.27
E
D
C B
A
Relaxed state
Contracted state
Full contracted
state
4.02.5
Sarcomeres
greatly
shortened
Sarcomeres at
resting length
Sarcomeres excessively stretched
Fig. 4 Schematic diagram of the optical muscle sensing system, where (1) a PANDA ring resonator, the comparison of signals
between the reference and the sensing signals in non-perturbed situation (2), and in perturbed situation (3).
Input port Throughput port
EL1
EL2ER1
ER2
E4
ELRL
K3
K1
K2
KO
E2
E3
E1
E1
RR
ER
Rad
Reference unit
Drop port
Drop port
Sensing unit
Add portDrop port non-perturbed
perturbed
Reference signals
Sensing signals
λ
7.5e-05
6.5e-05
5.5e-05
4.5e-05 1.55e-06 1.56e-06
Wavelength
1.55e-06 1.56e-06
Wavelength
Intensity (a.u.)
7.5e-05
6.5e-05
5.5e-05
4.5e-05
Intensity (a.u.)
3
2
1
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the reference signal (blue line) and the sensing signal
(red line) are plotted, as shown in Fig. 4(2), which
demonstrates that the peaks of the wavelengths of
both signals have not shifted. In the second case, the
part of the sensing unit (RR) that has been perturbed
by the muscle contraction, can also be simulated by
introducing Gaussian beams into the input port of the
PANDA ring resonator circuit. A comparison done at
the drop port of both units, clearly indicates that the
peak of the wavelength within the reference unit and
the sensing unit has shifted, as shown in Fig. 4(3).
The relationships between the change in wavelength
and the perturbation of the muscle contraction can be
explained through the principle of the change within
the optical path length. Because the muscle contraction
mechanism to perturb the sensing unit (RR) is the cause
of the change affected within the optical path length (L),
this directly affects the wavelength shift (∆λ) by Eq. (1).
here, m is an integer, n is the refractive index of the
guiding material, and L is the optical path length of the
ring resonator.
For the relationship between the force and the
change in the sensing unit RR, the length is described
by
where F is the applied force, Y0 is the Young’s modulus
and A0 is the initial cross-section area. L0 is the initial
length and (∆L) is the change in length. Thus, the
sensor principles can apply to muscle measurements.
The linear relationship between the applied force and
the wavelength shift (∆λ) is formed. This linearity
indicates that this relationship is appropriate for use in
the Optical Muscle Sensing System as shown in Fig. 5.
Facial Gesture Signal Generation
Our system was based on the assumption that the
Optical Muscle Sensing System can sense and measure
facial gestures based on the contraction of the facial
muscle used in the gesture. In this research, we divided
the facial muscles into 8 facial gestures which are
formed by the facial muscles. We observed the reaction
and response to the facial muscles. These relationships
between the facial gesture and the particular effective
muscle or muscles are well known. This relationship is
shown in Table 1.
We identified eleven locations for placing the
sensing probes which provide feedback to the system.
The data collected using these probes are used by the
Optical Muscle Sensing System in order to detect and
measure the individual facial gesture signals and to
conrm that they are appropriately similar to the real
situation. As illustrated in Fig. 6, these eleven locations
cover all of the main facial muscle components used
in the mechanisms of facial expressions and facial
Table 1 The facial gestures and effective muscles [33-35]
Elementary facial gestures Facial muscle involved
Closing left eye
Orbicularis oculi (left)
Corrugator supercilii (left)
Frontalis (left)
Procerus
Closing right eye
Orbicularis oculi (right)
Corrugator supercilii (right)
Frontalis (right)
Procerus
Closing both eyes
Orbicularis oculi
Corrugator supercilii
Frontalis
Procerus
Pull up the eyebrows
Levator palpebrae superioris
Corrugator supercilii
Frontalis
Procerus
Smiling
Zygomaticus major
Zygomaticus minor
Risorius
Buccinator
Smiling with left side
Zygomaticus major (left)
Zygomaticus minor (left)
Risorius (left)
Buccinator (left)
Smiling with right side
Zygomaticus major (right)
Zygomaticus minor (right)
Risorius (right)
Buccinator (right)
Clenching molar teeth
Zygomaticus major
Zygomaticus minor
Risorius
Buccinator
y= 0.0006x
R2= 1
0 5 10 15 20 25
Force (pN/m2)
30
λ/nm
35
20
18
16
14
12
10
08
06
04
02
0
Fig. 5 Graph of the linear relationship between force and the
wavelength shift (∆λ).
λm
λ
m
=n
n+L
L (1)
F=Y0A0
L0L (2)
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gestures. We did confirm that the attributes of these
eleven locations, and the data returned by the probes,
are sufficient for classification of all facial gestures.
The location of the probes included the frontal is
(occipitofrontalis) muscle, for probes P1 and P2, the
orbicularis oculi and corrugator supercilii muscles
for probes P3 and P4, the procerus muscle for probe
P5, the zygomaticus minor and masseter muscles for
probes P6 and P7, the zygomaticus major muscle for
probes P8 and P9, and the risorius muscle for probes
F10 and P11.
The next step in the development of the Optical
Muscle Sensing System, was to dene the method of
calculating the length of sarcomeres in each degree of
muscle contraction. The degrees of shortening of the
sarcomeres within the muscle contraction mechanism
depends on the transformation of the sarcomeres
which is based on the nature of the cross-bridge theory.
Similarly, the length-tension of the muscle for each
degree of muscle contraction is based on the shortening
of the sarcomere, as shown in Fig. 3. The length of the
sarcomeres is therefore given by functions (3), (4) and
(5),
Srelaxed = ∆SAB = 3.65 2.25 = 1.40 (3)
Scontracting = ∆SBD = 2.25 1.67 = 0.58 (4)
Sfull-contracted = ∆SDE = 1.67 1.27 = 0.40 (5)
where Srelaxed, Scontracting, and ∆Sfull-contracted are the
lengths of sarcomeres in relaxed status, initiated or
contracting status, and fully contracted status. The
percentage of the length of the sarcomeres can be
obtained by multiplying the length of the sarcomere by
100 and divide by ∆Srelaxed + ∆Scontracting + ∆Sfull-contracted as
shown in functions (6), (7) and (8),
For example, %∆Srelaxed = (1.40×100)/(1.40 + 0.58
+ 0.40) = 58.82%, %∆Scontracting and %∆Sfull-contracted
are 24.37% and 16.81%, as shown in Column 2 of
Table 2. We have determined the degrees of sensor
impacts based on the degrees of the sliding filament
mechanism, which is consistent with the degrees of
facial muscle contraction. In the previous paragraph,
the calculated methods of the lengths of sarcomeres
and the percentage of the lengths of the sarcomeres
were presented.
Next, the degrees of the perturbed optical muscle
sensing system based on the degrees of the sliding
filament mechanism, which is consistent with the
degrees of the facial muscle contraction are explained
and dened. In order to the degrees of the perturbation
have an effect on the sensing system was similar and
consistent with the actual degrees of the shortening of
sarcomere lengths. Hence, we have divided the degrees
of sensor perturbations into three levels include the
“Affecter”, which was used for the contraction of
Procerus
Corrugator supercilii
Zygomaticus minor
Buccinator
Risorius
Zygomaticus major
Masseter
Orbicularis oculi 2
45
6
89
7
10 11
3
1
Frontalis
Fig. 6 Sites of 9 implicated facial muscles and 11 the feature probes.
%∆Sfullcontracted =Sfullcontracted ×100
Srelaxed+∆Scontracting+∆Sfull
contracted (8)
%∆Scontracting=Scontracting×100
Srelaxed+∆Scontracting+∆Sfull
contracted (7)
%∆Srelaxed=Srelaxed×100
Srelaxed+∆Scontracting+∆Sfull
contracted (6)
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muscles in a relaxed state, the “Minor” which was
used for the contraction of muscles in contracting state,
and the “Major” which was used for the contraction
of muscles in a fully contracted state. This is shown in
Column 3 of Table 2. In the same way, the differences
in the perturbed levels during the contraction of each
the facial muscle is being formed, which is be based on
the facial gestures are shown in Table 3. As identied
in Fig. 6, we can assume that the facial gesture “Pull up
the eyebrows”, for example, is controlled by particular
muscles and the degrees of stimulation force can be
sensed by the various probes in the affected areas.
Overall, the affect (perturbed level) of each particular
facial gesture on each probe is shown in Table 3.
Therefore, any specific facial gesture will affect the
particular probes which measure the stimulation force
asserted on the facial muscle by the facial gesture.
Simulation Results
To form the simulation of the facial gesture
measurement by using the optical muscle sensing
system, the parameters of the PANDA ring resonator
were fixed to be the Gaussian beams wavelength of
1.55 (µm) and power of 10 mW were released into the
input port of the PANDA ring resonator circuit. The
waveguide material used is InGaAsP/InP, with core
index is n0 = 3.14, core area of waveguide is Aeff = 0.3
μm2 and waveguide loss coefcient is α = 0.1 dBmm¯1.
The coupling coefficient ratios were κ0 = 0.018, κ1 =
0.44, κ2 = 0.92, κ3 = 0.39.
In this simulation work, in order to provide a range
of random radii consistent with the nature of the
degrees of the sliding lament mechanism, we dene
the rst ring is arranged as a reference ring (reference
unit), with radius RL = 3.2499 µm. The second ring RR
is the sensing ring (sensing unit), which was position
as the ring perturbed by the applied force (the muscle
contraction). The ring radii varied from 3.2499 µm
to 3.2540 µm and the third ring is used to form the
interference signals between signals from the first
ring and the second ring, with the radius Rad = 12 µm.
Therefore, an “Affecter” degree of muscle perturbation
is the effect caused by muscle contraction in the
relaxation state’s of the degrees of sliding filament
mechanism. This perturbation has a random range of
radii between 3.2499 µm and 3.2522 µm. The “Minor”
and the “Major” of muscle perturbation are the effects
caused by muscle contraction when contracting to
the fully contracted state. These perturbations have a
Table 2 The degrees of the sliding lament compared to the degrees of sensor perturbations
Degrees of the sliding lament mechanism The percentage of sarcomeres length Degrees of sensor perturbations
Relaxed 58.82 Affecter
Contracting 24.37 Minor
Fully contracted 16.81 Major
Table 3 The degrees of affect various probe
Gesture Name
Perturbation level of probes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
Closing both eyes Minor Minor Major Major Major Minor Minor Minor Minor Affecter Affecter
Closing left eye Major Minor Major Affecter Major Affecter Major Minor Major Affecter Minor
Closing right eye Minor Major Affecter Major Major Major Affecter Major Minor Minor Affecter
Pull up the eyebrows Major Major Major Major Minor Minor Minor Minor Minor Affecter Affecter
Smiling Affecter Affecter Minor Minor Affecter Major Major Major Major Minor Minor
Smiling with left side Minor Minor Major Affecter Minor Affecter Major Minor Major Affecter Major
Smiling with right side Minor Minor Affecter Major Minor Major Affecter Major Minor Major Affecter
Clenching molar teeth
(rage) Minor Minor Minor Minor Affecter Major Major Major Major Minor Minor
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random range of radii between 3.2523 µm and 3.2533
µm for the “Minor” state and 3.2534 µm to 2540 µm
for the “Major” state. This is shown in Column 3 of
Table 4.
In Table 4, the random range of radii is obtained
from the relationship between the degrees of the
perturbation, which occurred on the sensing unit (ER)
within the optical muscle sensing system and the
percentage of the sarcomere length. So, while the eight
facial gestures (listed in Table 3) was conducted the
signals of the facial gesture is collected from all probes
obtained the different degree of sensor perturbation
is placed in various positions on the face. In terms of
measurement methods, these received different signals
are based on the difference of the facial gestures. In
order to make the facial gesture signal values for the
simulation as accurate as the signal values collected
from the measurements of the actual situation, we have
randomized the range of the second ring (sensing unit)
radius based on the degree of sensor perturbation from
1,500 occurrences of each facial gesture, as shown
in Fig. 7. In addition, individual physical differences
will directly affect the movement of the facial gesture
in real situations. We have demonstrated comparing
the percentage of varied overlap. This relationship is
shown in Table 5. Typical simulation results of overlap
radii generated are shown in Fig. 8-11.
Data Interpretation and Discussion
From the previous section, the simulation results
of the facial gesture demonstrate the differences from
a wide variety of data, both in terms of the amount
of data and the volume of the parameter, as shown
in Fig. 7-11. In Fig. 7 shows an example of signal
data pattern detected by the 11 probes, which have a
different radius based on the degrees of perturbation.
The “closing both eyes” gestures effect a “Major”
degrees in probes “P3”, “P4”, and “P5” “Minor”
degrees in probes “P1”, “P2”, “P6”, “P7”, “P8”, and
“P9” and other probes are “Affecter” degrees, which
is the radius of the sensing ring is random in the
range of 3.2534 µm-3.2540 µm, 3.2523 µm-3.2533
µm and 3.2499 µm-3.2522 µm, respectively. The
results of the data that has been created in the first
record of the “closing both eyes” gesture to have the
Table 4 The relationship between the degrees of sensor impact
and the random range of radius
Degree of sensor
perturbation
The percentage of the
sarcomere length Random range of radii
Affecter 58.82 3.2499-3.2522
Minor 24.37 3.2523-3.2533
Major 16.81 3.2534-3.2540
P1
3.2533
3.2529
3.2533
3.2529
3.2525
3.2524
3.2524
3.2531
P2
3.2524
3.2523
3.2526
3.2524
3.2531
3.2531
3.2526
3.2526
P3
3.2537
3.2536
3.2539
3.2539
3.2539
3.2536
3.2534
3.2538
P4
3.2538
3.2537
3.2536
3.2539
3.2539
3.2538
3.2539
3.2536
P5
3.2534
3.2535
3.2537
3.2540
3.2536
3.2536
3.2535
3.2538
P6
3.2525
3.2533
3.2528
3.2527
3.2532
3.2524
3.2531
3.2525
P7
3.2529
3.2526
3.2528
3.2527
3.2526
3.2533
3.2528
3.2524
P8
3.2531
3.2529
3.2530
3.2529
3.2530
3.2527
3.2533
3.2532
P9
3.2532
3.2529
3.2531
3.2524
3.2528
3.2523
3.2525
3.2532
P10
3.2511
3.2505
3.2504
3.2505
3.2515
3.2509
3.2517
3.2520
P11
3.2511
3.2516
3.2417
3.2508
3.2510
3.2519
3.2504
3.2500
1
2
3
4
5
6
7
8
3.2523
3.2532
3.2525
3.2528
3.2529
3.2526
3.2526
3.2525
3.2527
3.2532
3.2539
3.2538
3.2536
3.2538
3.2535
3.2537
3.2539
3.2539
3.2535
3.2534
3.2535
3.2534
3.2539
3.2537
3.2534
3.2527
3.2526
3.2533
3.2532
3.2529
3.2533
3.2530
3.2532
3.2533
3.2524
3.2533
3.2531
3.2530
3.2529
3.2527
3.2527
3.2526
3.2530
3.2532
3.2529
3.2518
3.2509
3.2517
3.2515
3.2520
3.2515
3.2520
3.2513
3.2513
3.2505
1496
1497
1498
1499
1500
...
...
...
...
...
...
...
...
...
...
...
Fig. 7 Non-overlapping in “closing both eyes” gestures.
Table 5 The random range of radius based on the percentage of overlap radius
Degree of perturbation Random range of radii
The percentage of overlap radii
20% 30% 40% 50%
Affecter 3.2499-3.2522 3.2499-3.2524 3.2499-3.2525 3.2499-3.2526 3.2499-3.2528
Minor 3.2523-3.2533 3.2518-3.2534 3.2516-3.2535 3.2513-3.2536 3.2510-3.2537
Major 3.2534-3.2540 3.2532-3.2540 3.2531-3.2540 3.2530-3.2540 3.2528-3.2540
177
Nano Biomed Eng 2015, Vol. 7, Issue 4
http://www.nanobe.org
signal data pattern in each probe from “P1” to “P11”
is “3.2533”, “3.2524”, “3.2537”, “3.2538”, “3.2534”,
“3.2525”, “3.2529”, “3.2531”, “3.2532”, “3.2511”,
and “3.2511”, respectively. In the second record of
the same gesture, the data signal received is “3.2529”,
“3.2523”, “3.2536”, “3.2537”, “3.2535”, “3.2533”,
“3.2526”, “3.2529”, “3.2529”, “3.2505”, and “3.2516”,
respectively. So, the twelve thousand signal data
patterns were obtained from 11 probes of the 8 facial
gestures through the simulation of facial gestures 1500
times in one gesture. In addition, we also recognize
the individual differences theory because these gesture
would have to be different in the amount of force used
in the process of muscle contraction. We assume that a
situation of overlapping in degrees of perturbation has
occurred, it has defined as the ratio of the overlap is
20%, 30%, 40%, and 50%, which results are shown in
Fig. 8-11, respectively. The results of these simulations
P1
3.2533
3.2528
3.2527
3.2521
3.2526
3.2534
3.2526
3.2523
P2
3.2521
3.2524
3.2526
3.2522
3.2530
3.2531
3.2522
3.2531
P3
3.2537
3.2535
3.2538
3.2532
3.2538
3.2538
3.2535
3.2533
P4
3.2536
3.2536
3.2534
3.2535
3.2533
3.2536
3.2540
3.2533
P5
3.2538
3.2533
3.2535
3.2537
3.2537
3.2535
3.2539
3.2539
P6
3.2532
3.2523
3.2527
3.2528
3.2519
3.2534
3.2530
3.2533
P7
3.2519
3.2533
3.2525
3.2527
3.2521
3.2519
3.2524
3.2525
P8
3.2522
3.2525
3.2527
3.2518
3.2522
3.2522
3.2529
3.2521
P9
3.2526
3.2532
3.2519
3.2529
3.2532
3.2523
3.2520
3.2522
P10
3.2521
3.2509
3.2503
3.2521
3.2505
3.2512
3.2500
3.2504
P11
3.2508
3.2524
3.2499
3.2511
3.2505
3.2512
3.2502
3.2523
1
2
3
4
5
6
7
8
3.2531
3.2519
3.2519
3.2524
3.2529
3.2531
3.2521
3.2526
3.2524
3.2527
3.2539
3.2539
3.2539
3.2540
3.2533
3.2539
3.2532
3.2533
3.2532
3.2536
3.2533
3.2540
3.2539
3.2538
3.2534
3.2526
3.2531
3.2531
3.2532
3.2520
3.2525
3.2532
3.2523
3.2530
3.2526
3.2534
3.2532
3.2522
3.2526
3.2531
3.2531
3.2524
3.2522
3.2529
3.2520
3.2503
3.2510
3.2513
3.2502
3.2520
3.2504
3.2508
3.2519
3.2520
3.2518
1496
1497
1498
1499
1500
...
...
...
...
...
...
...
...
...
...
...
P1
3.2530
3.2525
3.2523
3.2527
3.2516
3.2520
3.2534
3.2532
P2
3.2518
3.2530
3.2530
3.2516
3.2534
3.2528
3.2522
3.2534
P3
3.2536
3.2536
3.2535
3.2534
3.2539
3.2540
3.2534
3.2536
P4
3.2534
3.2537
3.2534
3.2532
3.2538
3.2540
3.2537
3.2535
P5
3.2538
3.2539
3.2533
3.2538
3.2532
3.2533
3.2531
3.2540
P6
3.2532
3.2518
3.2524
3.2520
3.2531
3.2530
3.2523
3.2524
P7
3.2518
3.2530
3.2518
3.2531
3.2529
3.2521
3.2522
3.2531
P8
3.2523
3.2524
3.2527
3.2531
3.2521
3.2530
3.2527
3.2527
P9
3.2530
3.2522
3.2520
3.2523
3.2535
3.2520
3.2529
3.2533
P10
3.2520
3.2506
3.2504
3.2525
3.2516
3.2500
3.2505
3.2517
P11
3.2509
3.2499
3.2513
3.2523
3.2508
3.2507
3.2510
3.2503
1
2
3
4
5
6
7
8
3.2527
3.2531
3.2523
3.2525
3.2518
3.2524
3.2517
3.2519
3.2522
3.2519
3.2533
3.2535
3.2535
3.2534
3.2538
3.2534
3.2531
3.2539
3.2538
3.2536
3.2536
3.2538
3.2539
3.2539
3.2537
3.2529
3.2530
3.2519
3.2532
3.2534
3.2523
3.2528
3.2517
3.2535
3.2519
3.2521
3.2529
3.2517
3.2528
3.2528
3.2525
3.2521
3.2529
3.2528
3.2532
3.2502
3.2517
3.2508
3.2514
3.2514
3.2521
3.2518
3.2525
3.2502
3.2518
1496
1497
1498
1499
1500
...
...
...
...
...
...
...
...
...
...
...
P1
3.2515
3.2521
3.2520
3.2533
3.2533
3.2529
3.2520
3.2528
P2
3.2516
3.2535
3.2533
3.2531
3.2528
3.2523
3.2522
3.2514
P3
3.2538
3.2537
3.2540
3.2532
3.2538
3.2534
3.2538
3.2532
P4
3.2535
3.2536
3.2532
3.2536
3.2538
3.2534
3.2532
3.2531
P5
3.2535
3.2533
3.2537
3.2534
3.2532
3.2536
3.2533
3.2537
P6
3.2528
3.2519
3.2523
3.2528
3.2514
3.2525
3.2517
3.2529
P7
3.2521
3.2518
3.2529
3.2523
3.2519
3.2520
3.2529
3.2534
P8
3.2514
3.2532
3.2527
3.2515
3.2534
3.2535
3.2515
3.2531
P9
3.2520
3.2535
3.2535
3.2516
3.2514
3.2533
3.2533
3.2535
P10
3.2517
3.2516
3.2512
3.2510
3.2526
3.2508
3.2515
3.2500
P11
3.2514
3.2507
3.2523
3.2512
3.2515
3.2523
3.2521
3.2512
1
2
3
4
5
6
7
8
3.2535
3.2523
3.2528
3.2525
3.2518
3.2517
3.2522
3.2520
3.2515
3.2525
3.2531
3.2531
3.2535
3.2539
3.2535
3.2534
3.2531
3.2539
3.2532
3.2533
3.2535
3.2531
3.2535
3.2538
3.2535
3.2526
3.2526
3.2515
3.2529
3.2522
3.2535
3.2520
3.2527
3.2521
3.2536
3.2532
3.2534
3.2535
3.2530
3.2527
3.2516
3.2535
3.2531
3.2524
3.2519
3.2518
3.2505
3.2526
3.2504
3.2521
3.2508
3.2520
3.2522
3.2512
3.2518
1496
1497
1498
1499
1500
...
...
...
...
...
...
...
...
...
...
...
Fig. 8 Overlapping 20% in “closing both eyes” gestures.
Fig. 9 Overlapping 30% in “closing both eyes” gestures.
Fig. 10 Overlapping 40% in “closing both eyes” gestures.
178 Nano Biomed Eng 2015, Vol. 7, Issue 4
http://www.nanobe.org
have shown clearly that there is a random distribution
of various radii of the ring and to cover the possibility
of the degrees of perturbation in a real situation. This
amount of data and volume of parameters caused
difficulties in the processing and interpretation of
the data. However, the various data problems can be
handled effectively by data mining techniques (DMT).
The classification function is one of the outstanding
techniques of data mining, which is used to classify
data into pre-identiable categorical class labels.
In application of data mining techniques, the extent
and variety of the facial gesture signals resulting from
the simulations show that these data mining methods
can be used in the analysis of this data, assisting in
the improvement and development of processes of
the assistive technology or tools in order to have the
ability of the signal pattern recognition based on the
machine learning approaches. This is important data to
be applied to research and resolve issues with various
assistance systems such as human-robot interaction
(HRI), human computer interaction (HCI), and human
machine interaction (HMI) in the near future.
Conclusions
The overall objective of our work is to demonstrate
an interesting approach of facial gesture measurement,
which can be a good candidate for optical sensing
system. In principle, the optical muscle sensing system
was performed based on the change in wavelength of
the optical sensor can be congured to the optical path
length variation within the PANDA ring resonator.
The results obtained from the simulation of the
facial gesture measurement are performed based on
the actual situation of the mechanism of the muscle
contraction. The relationship between the shortening
of the sarcomere and the sarcomere length was applied
as the basis concept to determine the perturbed degrees
of the muscle contraction, which is divided into three
levels, namely affecter, minor, and major. The process
of sampling to adjust the size of the radius within the
sensing unit will be used to demonstrate the changes
that occur within the sensing system after perturbed
by the muscle contraction. In future work, we intend
to apply these signal data were obtained from the
simulation with the machine learning approaches in
order to classify and recognize the facial gesture signal
pattern, which will be extremely useful for various
utilization such as hands-free human-computer
interactions and behavior monitoring which can be
used in many disability support applications.
Acknowledgements
This work is partially supported by the Faculty
of Science, Naresuan University, Thailand. Lastly,
the authors also gratefully acknowledge the Faculty
of Science, King Mongkut’s Institute of Technology
Ladkrabang, Thailand, for the laboratory and research
facilities. Many thanks to Mr. Roy Irvine Morien of the
Naresuan University Language Centre for his editing
assistance and advice on English expression in this
document.
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Copyright© 2015 Kriengsak Yothapakdee, Preecha P. Yupapin
and Kreangsak Tamee. This is an open-access article distributed
under the terms of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction
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credited.
... This is the in-vivo test, where the contact between the sensing device and face skin can be introduced the change in sensing unit optical path difference, which will be detected and rerated to the change in muscle movements, which can be patterned and classified for the use of face healthcare improvement. The facial gesture pattern recognition can be patterned and stored by the technique that has proposed by Yothapakdee et al. [28][29][30][31][32][33][34], which is useful for cosmetic material ingredient use, where the before and after cosmetic treatments can be monitored and evaluated for the required improvements. Because the face is a communication channel with non-verbal language through the process of facial expressions, gestures and emotions, so, if the biomedical characteristics of the face can be monitored and collected to have been extremely useful for research and technological development of medical science and more. ...
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Chapter
In order for organisms to live, survive and reproduce, they must search for and find a habitat that is appropriate in terms of chemical constitution, ambient temperature, food supply, availability of potential mates, optimal conditions for their offsprings, etc. This implies that they must exchange information with their ever-changing environment. The exchange process has two aspects: acquisition and delivery of information. Acquisition involves reception of signals related to many different facets of the environment. Delivery implies executive acts, be they motor, excretory or otherwise. In single-celled organisms like paramecia, both processes are carried out by the same cell and take place for the most part at their body surfaces, i.e., their cell membranes. In highly developed metazoa, cells have specialized functions, certain groups being “receivers” and others “executors” of information; in fact, most are both. Executors include muscle, glandular and other cells, because motor acts and glandular functions transmit information to the environment or other cells.
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