Augmented Reality based Body Sound Simulation
Dr. Chathura de Silva1, S.A.M Samarawickrama2
Department of Computer Science and Engineering, University of Moratuwa
Moratuwa, Sri Lanka
1chathura@uom.lk
2118229H@uom.lk
Abstract— Auscultation is the act of listening to body sounds and
using those captured abnormalities in diagnosing process. But
correctly recognizing and capturing the abnormalities from these
faint sounds, requires enormous experience and training, which
may not be feasible in all cases. This research is intended on
investigating the implementation of a body sound simulation tool
for medical students and other researches using augmented
reality based techniques. This tool will mainly be used as a
practicing tool for medical students to get familiarized with body
sound patterns associated with various disease conditions. Image
processing and digital sound processing techniques will be used
to create augmented reality environment and the tool will be
made configurable by experts.
Keywords—Auscultation, Augmented reality
I. INTRODUCTION
Sound has a long history in medicine and it’s considered to
be a very important and initial factor in diagnosing. Tools
used for auscultation ranges from usual stethoscope to taking
obstetric ultrasound images. Irrespective of the tool used, it
requires enormous experience and talent to extract factors
about the disease condition that the patient suffers from these
sounds, which are usually faint and contains unrecognizable
disorders which practically cannot be identified by an
untrained personal. Three main origins or sources of sound are
identified to be important in traditional auscultation process,
heart sounds, lung sounds and stomach/bowel sounds. Heart
sounds, or heartbeats, are the noises generated by the beating
heart and the resultant flow of blood through it (specifically,
the turbulence created when the heart valves snap shut).
Breath sounds originate in the large airways where air velocity
and turbulence induce vibrations in the airway walls. Stomach
sounds or abdominal sounds (bowel sounds) are made by the
movement of the intestines as they push food through. Among
those, heart sounds are considered to be the most important in
auscultation. Lung sounds are also considered to be having
wide variations and critical in the diagnostic process.
Our technique for simulating this internal and external
human body environment will mainly be using augmented
reality based techniques. Augmented reality (AR) is a
technology that combines, or augments, the real world with
additional virtual information which is overlaid on top of the
real world. Providing additional information to the user about
the world opens the possibility of a number of new application
areas. The main benefit of these applications is their ability in
aiding a user in performing tasks in the real world mainly
through simulations. The techniques described in augmented
reality can be directly put in effective teaching processes,
where simulation is most important in convincing some
concept to the student who is learning them. Augmented
reality techniques come handy where it can provide the
interactive simulations mixed with the real world scenarios.
Integrating hearing aids into the augmented reality
environment will make the simulation more effective.
II. BASIC MODULES
Basic modules of the simulation tool include a camera
interface module, audio generator module, stethoscope
module, main processor and the dummy with visual cues
Figure 1.
A.
Figure 1: Basic Modules
Camera module is used to grab interactive motion of the
user relative to the model by taking inputs from camera.
Specific predefined patterns are used both on stethoscope and
the model/dummy itself. After the location is verified heart
sound generation is done on the audio generator module.
Static sound map data configured and tested are used here.
Then this generated sound will be further processed based on
the location of the stethoscope. And the final heart sound
corresponding to the specific disease condition will then be
fed back to the stethoscope via wireless media. Optional status
information can be also taken from the stethoscope module,
such as the pressure applied on the stethoscope by doctor etc.
III. INTERFACING CAMERA MODULE, OBJECT REGISTRATION
AND MOVEMENT TRACKING
Camera position and illumination scheme was chosen
considering the real environment where the apparatus is
actually installed. Object registration was done using
augmented reality markers. Marker size, orientation, positions
and content were designed in such a way that they provide
transformation invariant object registration and maximum
occlusion handling. Entire process of object registration and
interaction determination was done in real time.
A. Important aspects of the real environment, illumination
scheme and camera positioning
The environment in which this simulation apparatus is
installed would be a hospital, or a similar place where the
dummy will be laid horizontally with one or more doctors and
other assistants standing nearby, examining the patient.
During the auscultation process the doctor will remain almost
stationary. The typical poses and approaches during
auscultation will block sideways ( Figure 2). But the top view
will be least occluded and may reveal many unblocked
regions. And since the doctor’s approach is always restricted
to sideways but never from the top or bottom ends during
upper body auscultation, we can consider top and bottom ends
also to be providing views with minimal occlusions.
shadowing effect was found to be the best illumination
scheme.
B. Robust registration of object and movement tracking using
circular markers
Without using traditional markers [8] or an existing toolkit,
markers which consist of circular shapes where the marker
patterns were chosen to be having close correlation but
significant absolute difference were used. Close correlation
between markers lead to choose different but known maker of
the same class during an occlusion of the pattern that is being
searched. But the significant difference between markers
provides a clue for dropping that selection, or provides a
means of making sure whether an actual occlusion has
occurred.
Figure 3: Tested marker patterns
Figure 2: Typical poses during auscultation (generic case)
So the optimal region for the camera to be fixed for
minimal occlusions/interference was found to be within the
shaded region in Figure 2. Also, placing the camera at the
doctor’s eye level helps to get more close emulation of the
doctor’s view.
An illumination scheme that minimizes external
interference which caused by shadows and other varying
lightening conditions has to be implemented for this scenario.
Reconsidering
Figure 2, it can be clearly seen that
illumination scheme which contains a light source on the
sideways of the patient’s bed would create more shadows than
using sources on top and/or bottom sides of the patient’s bed.
Since the chest area or the upper body has more importance in
this simulation, keeping source on the top side rather than
bottom, or keeping sources at both ends which cancels out any
Circular patters used for the markers in this research are
depicted in Figure 3. By tracking markers in the order A→ B
→ C, we will utilize the property of close correlation between
them in identifying occlusions. For example, matching for
pattern ‘ A ’ , with the presence of ‘B’ and ‘C’ would
provide best match location near pattern ‘A’ if it is not
occluded. During an occlusion either ‘B’ or ‘C’ will be
tracked due to their higher correlation with pattern ‘A’.
Tracking of pattern ‘A’ during occlusions and corresponding
difference values with original template ‘A’ is given in Table
1. Once a match is found we can exploit the fact that it’s
difference between the intended markers (marker ‘A’ in this
case), should always be less than the absolute difference with
other markers. And if the absolute difference is found to be
higher, then we can assume an occlusion.
TABLE 1: CORRELATION AND DIFFERENCE VARIATION DURING OCCLUSIONS
Marker A matched
(with presence and occlusion of itself and others)
A, B
and C
Markers present
Absolute Differences of
matched results with
marker
B and
C
C
only
A
609
908
854
B
820
501
823
C
918
753
483
C. Implementation
Standard web cam, which provides 320x240 images at a
maximum of 30fps, was selected as the camera. This module
was fixed rigidly around 5’ 6’’ feet (standard human height)
on top of the plane in which the dummy lies. This helped in
providing images which has least interruptions by doctor’s
actions, and also it simulated the actual viewpoint of the
doctor. Visual cues or augmented reality markers were placed
avoiding the process critical region. Marker on the
stethoscope is placed on the low frequency bell since it’s
found to be the place which is least occluded. Also an initial
calibration process is carried out in order to collect dummy
specific data such as relative locations of jugular notch,
xiphoid process and left and right coastal margins and marker
templates. Center marker template, right marker template, left
marker template and a template of marker placed on
stethoscope are also captured and stored during this process.
Since the used markers contains patterns with sharp edges
(higher gradient), gradient images was found to be providing
better details about the marker locations. Sobel operator is
used as the gradient operator (Equation 1).
Equation 1: Sobel masks used
The preprocessed template images with sobel operator
showed clear bimodal histograms where an optimal threshold
could always be found which separates the marker pattern
from its background. Otsu’s method for thresholding was
decided to apply on the original template and matched
template images and before calculating differences.
If at least two markers were tracked the dummy feature
matrix (which consists of locations of jugular notch, xiphoid
process left and right coastal margins) is filled using
calculated marker positions mapped onto initial calibration
data. Found marker positions and calculated parameters were
used with the following 2D transformation matrix; where α,
dx and dy are the rotation, x-offset and y-offset of the dummy.
Equation 2: Multiplication with 2D transformation matrix
Distance between jugular notch and xiphoid process,
distance between xiphoid process and lower margin of the rib
cage and the distance between left and right coastal margins
are taken as governing factors that decides uniqueness of the
physical structure of any human being. In order to support
different dummy structures the coordinate system of the
dummy is assigned with two different scale factors thus
dividing it into two parts (main points of separation - jugular
notch, xiphoid process and coastal margins). This separation
was also taken into account while calculating the final
location of the stethoscope on the body map where the sound
map will be augmented into.
IV. HEART, LUNG, BAWL SOUND VARIATION AND INTERFACING
HUMAN BODY SOUND MAP
Awareness of sound propagation patterns inside human
body is essential in configuring general sound map. Human
body has few distinct sound sources which will interpolate
themselves to produce a unique sound at a given location. But
this sound propagation patterns can be considered to be
unique for a person. Also the effects caused by abnormalities
are minute. So they are found to be best recognized and
interpreted by trained personals only.
A. Architecture of the sound map - decisions made and
justification
Decisions taken regarding the structure of the sound map…
 Jugular notch is assigned as origin. Locations of
common auscultation sites are known and marked.
 Some special points are assigned with original sounds
recorded from patient. And other locations will use a
dynamically generated audio signal. (usually generated
interpolating sounds at common auscultation sites)
 Recorded original sounds are assigned to corresponding
common auscultation sites.
 Sounds generated at other locations will be explained
using sounds associated with common auscultation sites.
 Sound map calibration will be done to assign simulated
sounds onto the sound map.
 Calibration of this sound map will be done by an expert
(experienced doctor).
 Each case and configuration (normal breath, breath held
up and deep breath) will contain a unique sound map.
 Each case map will be certified by a panel of experts
and apprentices.
 Error status is assumed when a sound corresponds to
any point other than non-auscultation site is requested.
But the generated sound will still be taken as the output.
Justifications…
 Doctors are mainly concerned about the sounds heard at
common auscultation sites. So it is better to use original
sounds at those points.
 Precise interpretation is not essential for other locations.
Since they are not be used in an auscultation process.
The interpretation given by experts can be considered to
be sufficiently correct.
 Minute changes which occur during abnormalities are
best interpreted by trained personals (predicting output
based on waveform analyzing is error prone and not
robust).
B. Implementation
Prerecorded body sounds with minimal noise are used for
constructing sound map. Separate sound maps were
constructed for each case and each configuration. Entire upper
body area is divided into several rectangular cells. Size and
location of rectangular cells are defined by an expert, and are
unique for each case. Audio signal within each cell is
considered to be constant. Prerecorded sound files are used at
common auscultation sites. Also the facility of using sounds
other than the sounds heard at common sites is provided.
Example map constructed for the case mitral valve stenosis is
depicted in Figure 4.
A. Observations
1) Case 01 - Normal operation
Figure 5: Error (absolute distance between tracked and optimal locations)
variation - Normal operation
Front view map
Back view map
Figure 4: Mitral valve stenosis sound map
Frames tracked correctly = 385 / 400
Overall percentage error during normal operation = 96.25%
2) Case 02 – low lightening conditions
V. OBSERVATIONS, RESULTS & ANALYSIS
The functionality, performance and robustness of the
camera module and the performance and accuracy of
generated sound maps were first separately evaluated. And
then a combined score is used to depict the overall accuracy.
A. Accuracy evaluation of used object registration and
stethoscope tracking techniques
In order to determine the percentage error, tracked location
coordinates are compared with manually determined optimal
locations. Various measures including the Euclidian distance
between manually marked optimal location and computed
location, percentage of frames in error and total effective error
percentage (percentage of critical errors, which actually
affects the process) are used in measuring the overall accuracy
of object registration and tracking process.
Sample videos had to be taken covering all possible
environmental conditions that might occur during auscultation
process. Typical hospital environment is assumed and some
possible environmental conditions were extracted by simple
observation and via discussions with medical resource people.
Performance evaluation was done for the following cases.
 Normal operation
 Environments with low brightness condition
 Fast and slow moving shadows
 Partial or full occlusions
 Rapid transformations
Samples were processed and tested separately. The visible
optimal location is then marked on each frame manually for
error calculation (Equation 3).
Figure 6: Error (absolute distance between tracked and optimal locations)
variation - Low lightening conditions
Frames tracked correctly = 363 / 400
Overall percentage error with low lightening = 90.75%
3) Case 03 – effect of shadows
Figure 7: Error (absolute distance between tracked and optimal locations)
variation -with shadows
Frames tracked correctly = 255 / 300
Overall percentage error with shadows = 85.00%
Equation 3: Location tracking error
4) Case 03 – Occlusions
Figure 8: Error (absolute distance between tracked and optimal locations)
variation – Occlusions
stethoscope handling mechanism in case of continuous
failures. But from the results gathered by testing the apparatus
under various environmental conditions, displays that the
tracking process is accurate and robust enough to meet the
requirements.
B. Accuracy evaluation of sound maps
Sound maps for calibrated cases were forwarded to a panel
of experts for testing and confirmation. During a test, the
selected panel member is exposed to all cases and his/her
response and comments regarding the accuracy of
representation of sound maps were recorded as a percentage.
And those responses were averaged in order to provide an
overview accuracy score for each case. Only some common
cases are calibrated and tested here. Average accuracy scores
for some simulated cases are depicted in Figure 10.
Frames tracked correctly = 281 / 300
Overall percentage error with shadows = 93.77%
5) Case 03 – Transformed object
Figure 10: Average percentage of accuracy - cases
Figure 9: Error (absolute distance between tracked and optimal locations)
variation – Transformations
Frames tracked correctly = 238 / 300
Overall percentage error with shadows = 79.33%
B. Failure scenarios
 Fast stethoscope movements
Fast and slow stethoscope movements are caused
by moving the stethoscope from one auscultation
site to another during auscultation process. Failures
during fast stethoscope movements do not affect
the overall accuracy of the simulation since
stethoscope has to be kept stationary during an
auscultation process.
 Occlusions of 2 or more markers on dummy
Shadows and user movements may cause two or
markers to be occluded simultaneously.
 Occlusion of the marker on the stethoscope
The manner in which the user holds the stethoscope
may cause marker on the stethoscope to be
occluded.
Above all are categorized as failures and system will not
update the new location instead keeps on using the old
location. User may recalibrate the system or use an alternate
Another test was conducted with a panel of final year
medical students (15 students). Testers were directly exposed
to a sound map which was selected in random, and they were
asked to identify the case which is being simulated. The
percentage of a case is successfully recognized by the panel of
testers is recorded. And this is also used to depict the accuracy
of sound map calibration (Figure 11).
Figure 11: Case Recognition percentage
From the above results it can be clearly seen that the
manual calibration of sound maps can match with the actual
perception of trained tester. And it also represented a general
solution agreed by the majority of testing panel. So it can be
concluded that manual calibration of sound maps is a success.
VI. CONCLUSION
Body sound simulation via augmented reality techniques
has several advantages over tradition auscultation simulation
procedures. Traditional simulators usually used static sound
sources fixed at some predefined locations inside a hollow
space. But the main problem with this type of traditional
simulators is the uncontrollability of the sound propagation
patterns. Usually body sounds originates inside enclosed
chambers or tubes and are localized and usually blocked by
other internal organs. So there will always be an undesirable
interference caused by nearby sources in traditional simulators.
This can be effectively managed in our approach. The sound
map is manually configured and the calibration process is
equipped with freedom sufficient enough to configure the
sound propagation path in an optimal manner. Some
abnormalities cause disorders in human physique which
results in ‘nonstandard’ organ sizes and locations. Counting
ribs is the most common method of finding common
auscultation sites. But if the doctor suspects that the heart has
enlarged, then he usually will not depend on the rib count for
finding relevant auscultation site. Common auscultation sites
are also made configurable in our approach, making it
possible for the augmented sound map to deal with internal
structural changes. So augmented sound map techniques
provide more accurate and realistic simulations than
traditional simulators. Traditional simulators with high end
inbuilt speakers and sound mixers usually contains at least
some costly hardware. Whereas this augmentation approach
only requires consumer level web camera, dummy, wireless
speakers or similar mechanism to transmit generated sound to
the user stethoscope and some visual cues that can be acquired
with low or no cost. And the calibration process is easy and
purely software based.
But also there are some problems and disadvantages. The
method used for calibrating sound map was mainly based on
the perception and experience of the calibrator. During
calibration the calibrator will put more weight on the features
of abnormalities that are more profound for him. But that
sound pattern may not be found as the unique recognizer
associated with that simulated disease condition, by others. So
the method of calibration must be made standardized to make
everyone happy.
Overall performance and the accuracy score of the
simulation were found to be sufficient enough. Stethoscope
tracking process displayed high success rates (over 80%) for
all tests conducted under various environmental conditions.
User feedback on simulated cases and finally on overall
simulation environment was also found to be positive.
augmented reality techniques may be able to provide more
accurate and universal behavior.
Finding better techniques to improve the robustness of
object registration and tracking process is another
enhancement. The performance of 3D markers, techniques
which utilizes more visual cues, defining marker patterns with
better occlusion invariance etc. can be tested there.
VII.
FUTURE WORK
A method, solely based on human experience is used for
sound map calibration. This can be standardized by either
using a method that uses the internal structure of the human
body in calculating sound propagation model. This was found
to be an untouched research area where loads of new facts are
yet to be discovered. With a proper sound propagation model,
[17]
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