Effect of time delay on telesurgical performance.
ABSTRACT In the area of surgical robotics no standard means of performance evaluation has been established. Thousands of surgeons have gone through the SAGES FLS Program, and the psychomotor skill portion of the program is considered the gold standard in laparoscopic skills evaluation. This research describes the use of the FLS block transfer task to evaluate the performance of both surgeons and non-surgeons teleoperating under different time delay conditions on the University of Washington RAVEN Surgical Robot. Time delays of 0 ms, 250 ms, and 500 ms were used and a statistically significant difference in mean block transfer time as well as mean tool tip path length were shown. For this task no significant difference was shown between the surgeon and non-surgeon groups. Clearly surgeon input and feedback is key to surgical robotic system development, but this result implies that non-surgeon subjects can be tested for simple usability evaluations.
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ABSTRACT: The primary limitation of telesurgery is the communication latency. Accurate and detailed data are lacking to reveal the latency effects on surgical performance; furthermore, the maximum acceptable latency in telesurgery remains unclear. Sixteen medical students performed an energy dissection exercise and a needle-driving exercise on the robotic simulator dV-Trainer(®), and latencies varying between 0 and 1,000 ms with a 100-ms interval were randomly and blindly presented. Task completion time, instrument motion, and errors were automatically recorded. The difficulty, security, precision, and fluidity of manipulation were self-scored by subjects between 0 and 4 (0 the best, 2 moderate, and 4 the worst). Task completion time, motion, and errors increased gradually as latency increased. An exponential regression was fit to the mean times and motions (R (2) > 0.98). Subjective scorings of the four items were similar. The mean scores were less than 1 at delays ≤200 ms, then increased from 1 to 2 at 300-700 ms, and finally approached 3 at delays above. In both exercises, latencies ≤300 ms were judged to be safe by all and 400-500 ms were accepted by 66-75 % of subjects. Less than 20 % of subjects accepted delays ≥800 ms. The surgical performance deteriorates in an exponential way as the latency increases. The delay impact on instrument manipulation is mild at 0-200 ms, then increases from small to large at 300-700 ms, and finally becomes very large at 800-1,000 ms. Latencies ≤200 ms are ideal for telesurgery; 300 ms is also suitable; 400-500 ms may be acceptable but are already tiring; and 600-700 ms are difficult to deal with and only acceptable for low risk and simple procedures. Surgery is quite difficult at 800-1,000 ms, telementoring would be a better choice in this case.Surgical Endoscopy 03/2014; · 3.31 Impact Factor
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ABSTRACT: Abstract Background: Advances in robotics have made teleoperated surgical procedures a feasible means of treating patients in remote locations. In this study a suite of experiments was performed to investigate the influence of time-delay on teleoperation accuracy and efficiency during a path-following task. Materials and Methods: Subjects used a Phantom Omni 6-degrees of freedom (dof) input device (Sensable, Triangle Park, NC) to move the end-effector of a Mitsubishi (Tokyo, Japan) PA-10 7-dof robotic manipulator along a prescribed path. End-effector motion was recorded using a video motion capture system. Time-delays ranging from 0 to 2.5 s were artificially imposed. Performance was quantified by time to complete the task, path length, and square root-mean-square (RMS) error. Randomization of time-delay order and allowance for practice runs reduced the learning effect. An imposed time limit and pacing were used to negate the move-and-pause strategy that emerged in early trials. Results: Time to complete the task and RMS error generally increased with increasing time-delay. Path length also generally increased, but not as consistently. With imposed pacing, RMS error continued to increase beyond 1.5 s, and some subjects were not able to complete the task in the allotted 90 s. Conclusions: The results suggest a threshold of time-delay in the range of 1.5-2.0 s. Beyond 1.5 s, subjects adopted a move-and-pause strategy that increased completion time to preserve path-tracking accuracy. If paced, tracking accuracy tended to degrade substantially beyond 1.5 s. A strong learning effect was evident, and experienced teleoperators performed substantially better than novices.Telemedicine and e-Health 10/2014; · 1.54 Impact Factor
Conference Paper: Effect of backlash on surgical robotic task proficiency[Show abstract] [Hide abstract]
ABSTRACT: Teleoperated surgical robot designs, particularly those utilizing cable drive mechanisms, often exhibit motion backlash. This paper presents an experiment that investigates how backlash affects proficiency in basic surgical skills. Backlash was added to the Laprotek surgical robot using software, and subjects completed three Fundamentals of Laparoscopic Surgery (FLS) tasks. Three subjects performed the tasks under five different levels of system backlash (1, 5, 10, 15, and 20 mm). Subjects were able to complete the tasks with all levels of backlash. A linear relationship was observed between the amount of backlash in the system and task completion time. On average, completion times increased by 5% for every 1 mm of additional backlash in the system.Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference on; 01/2012
Effect of Time Delay on TeleSurgical Performance
Mitchell J.H. Lum, Jacob Rosen, Thomas S. Lendvay, Mika N. Sinanan, Blake Hannaford
Abstract—In the area of surgical robotics no standard means
of performance evaluation has been established. Thousands of
surgeons have gone through the SAGES FLS Program, and
the psychomotor skill portion of the program is considered the
gold standard in laparoscopic skills evaluation. This research
describes the use of the FLS Block Transfer task to evaluate the
performance of both surgeons and non-surgeons teleoperating
under different time delay conditions on the University of
Washington RAVEN Surgical Robot. Time delays of 0ms,
250ms, and 500ms were used and a statistically significant
difference in mean block transfer time as well as mean tool tip
path length were shown. For this task no significant difference
was shown between the surgeon and non-surgeon groups.
Clearly surgeon input and feedback is key to surgical robotic
system development, but this result implies that non-surgeon
subjects can be tested for simple usability evaluations.
Robot assisted surgery has revolutionized the way in
which many surgical interventions are performed resulting
in better patient outcomes. Telesurgery on a human patient
was accomplished on September 9, 2001 by Marescaux and
Gagner. In collaboration with Computer Motion, they used
a modified Zeus system to teleoperate between New York
City and Strasbourg, France under a 155ms time delay using
a dedicated Asynchronous Transfer Mode (ATM) commu-
nication link . Although this was a one-time experiment,
telesurgery has the potential to deliver expert surgical care
to anywhere in the world.
A. Active Research
In Asia, a group from the University of Tokyo has re-
cently been working on a new telesurgery system , and
has completed laparoscopic cholcystectomy on a porcine
model between sites in Japan, and moret recently between
Japan and Thailand. They state the experimental result as
“a laparoscopic cholecystectomy on a pig was successfully
carried out. The completion time of the surgery was about 90
min, which is roughly equal to a conventional laparoscopic
Morel’s group from University of Paris, Laboratoire de
Robotique de Paris (LRP) uses a spherical mechanism similar
to the RAVEN . Their device is relatively simple, but
what is novel is that it moves the trocar in addition to the
tool. This has allowed them to embed force sensors in the
M. Lum, B. Hannaford, Electrical Engineering, University of Washington,
Seattle, WA USA <mitchlum,blake>@u.washington.edu
J. Rosen, Computer Engineering, University of California, Santa Cruz,
CA USA email@example.com
T. Lendvay, Dept. of Urology, Seattle Children’s Hospital, Seattle, WA
M. Sinanan, Dept. of Surgery, University of Washington, Seattle, WA
device that give a direct reading of the forces at the tool tip,
instead of the combined interaction forces of the tool/tissue
Berkelman, at the University of Hawaii, Manoa, has fur-
ther developed the Light Endoscopic Robot (LER), on which
he began work in TIMC-IMAG laboratory in Grenoble.
This device was originally designed to guide an endoscopic
camera, but is now capable of holding disposable endoscopic
graspers . A tool with wrist articulation is currently in
B. SAGES Fundamentals of Laparoscopic Surgery
The Society of American Gastrointestinal Endoscopic Sur-
geons (SAGES) formed a committee for the Fundamentals
of Laparoscopic Surgery (FLS) in the late 1990’s. Peters 
discusses the background behind the development of the FLS
program. The program features both cognitive surgical under-
standing as well as hands-on technical skills. The technical
skills set is derived from the McGill University MISTELs
program . The FLS program has been implemented around
the world with thousands of surgeons from first year residents
to veteran surgeons tested. It provides a well structured and
well defined quantitative means by which a surgeon’s skill
can be evaluated.
C. TeleRobotic FLS
The UW RAVEN Surgical Robot is a system designed
for telesurgical applications. During its initial validation and
field experiments, the RAVEN was tested in a number of
teleoperation modes including operating through a digital
data link on board an unmanned aerial vehicle  and in
the Aquarius Undersea Habitat . The system has also
teleoperated with the Patient Site in Seattle, WA and the
surgeon site located in Cincinnati, OH; Tokyo, Japan; Mont-
pellier, France; and London, England connecting through
commercial Internet. Network statistics were collected on
packet loss, delay and correlation. These early experiments
provided motivation to more carefully study the delay effect
in telesurgical performance.
This manuscript describes the method by which the FLS
Block Transfer (Pegboard Transfer1) was adopted as a stan-
dardized task for telerobotic surgical performance evaluation.
Previously, a pilot study with only three non-surgeon subjects
was performed to obtain preliminary results and debug the
1The Pegboard Transfer is often referred to in short as “Peg Transfer,”
which has lead to some confusion, since blocks are being transferred not
pegs. Some surgeons refer to the task as “Block Transfer” to make the name
more descriptive of the task performed. For TeleRobotic FLS we use Block
Transfer synonymously with the SAGES Pegboard Transfer.
method . This study reports the results of six surgeons
and nine non-surgeons performing the Block Transfer task
under three different time-delay conditions.
Of the five FLS skills tasks, three are directly applicable
for use with surgical robotic systems and have been adopted
into the TeleRobotic FLS scheme. The tasks are Block
Trasfer, Intracorporeal Knot Tying, and Pattern Cutting. This
research used the Block Transfer task. This section describes
the experimental methods in detail.
A. TeleRobotic Block Transfer
In the SAGES FLS Block Transfer, the subject is allowed
to start with all blocks on either side, then transfer all blocks
from one side to the other and then back again. It is only
considered an error if a block is dropped outside the viewable
area, bounded by a black rectangle on the task board (see
Figure 1). The score is a proprietary formula based on an
aggregate of completion time with a penalty for errors.
Fig. 1.FLS Blocks being transferred using RAVEN
The TeleRobotic Block Transfer, by contrast, is more
structured to eliminate variability between subjects due to
different or evolving “block moving strategies.” A single trial
consists of moving all six blocks from the left side to the
right side and then back again. There is a pause between left-
to-right and right-to-left. Each peg is numbered as shown in
Figure 2. The trial begins with the tools touching or near
the first block. The blocks must be moved in numeric order
from the peg on one side to the corresponding peg on the
other side. Should the subject attempt to move blocks out of
order or to the wrong peg, the experimenter will remind the
subject that order matters.
Errors are defined as any time a block is not set down on its
corresponding numbered peg. This often occurs in the form
of a dropped block. If a block is dropped and recovered,
that is marked as one type of error. If a block is dropped
and not recovered that is noted as another type of error. For
system development purposes the experimenter should note
the cause of the error if that can be immediately determined.
When the experimenter says “Go!,” the time starts. The
individual “lap time” for each block is recorded and mea-
sured at the point where the block is fully in contact with
the surface of the pegboard (i.e. not partially dangling on
the peg or partially sitting on an adjacent block). The time
DISTANCES BETWEEN PAIRS OF PEGS ON THE FLS BLOCK TRANSFER
ends when the sixth block is placed. From the “lap times,”
the “split times,” or individual block transfer times, may be
The time-based results can either be reported as the
average individual block transfer time or the average time
for all six blocks, as well as the number of blocks dropped
and recovered and the number of blocks dropped and not
recovered. Optimally the surgeon performs the task carefully
enough that no errors occur.
UW BioRobotics Lab?
Fig. 2.TeleRobotic FLS peg numbering
B. Time Delay Experiments with the UW RAVEN Surgical
This section describes a specific implementation of the
TeleRobotic FLS methodology to study the effect of time
delay on performance using the UW RAVEN Surgical Robot
. This experimental set-up was used for both the pilot
study  and this study.
Real Teleoperation: In an actual teleoperation, physical
distance and a real network separate the patient and surgeon
sites with time-varying delays. When a surgeon makes a
gesture using the master device, motion information is sent
through the network to the Patient Site with a network time
delay (Tn). The manipulator moves and the audio/video
(a/v) device observes the motion. Digital a/v is compressed
(Tc), sent from the Patient Site to the Surgeon Site through
the network (Tn), then decompressed (Td) and observed
by the surgeon. The surgeon has experienced a total delay
T = 2Tn+ Tc+ Td, from the time (s)he made the gesture
to the time that action was observed.
Emulated Teleoperation: In the emulated teleoperation,
the Surgeon and Patient sites are not separated by physical
distance. Instead they are connected through a Linux PC
with two network cards running NISTNET that emulates
a real network. This emulator allows the experimenter to
adjust the average packet delay between the Surgeon and
Patient sites . The a/v feed is connected directly from
the camera at the Patient Site to the monitor at the Surgeon
Site through S-video eliminating any delay due to compres-
sion/decomression. The surgeon experiences a total delay, Te
due to the emulator, from the time (s)he made the gesture to
the time that action was observed.
Patient Site?Surgeon Site?
Patient Site?Surgeon Site?
T?e? = 2*T?n? + T?c? + T?d?
Fig. 3.Teleoperation communication flow
The flow of information is illustrated in Figure 3. By
setting Te = 2Tn+ Tc+ Td, one can emulate any real
teleoperation condition. In this study, because the camera
is connected directly to the monitor, there is no degradation
of the video or audio signals due to compression techniques.
Performance in telesurgery as a function of video degradation
could be the subject of a future study, but is not a factor in
1) Experimental Set-up: The RAVEN Patient and Surgeon
sites are located in the same room and are connected through
the network emulator. The video feed comes directly from a
Sony DCR-VX1000 3-chip digital video camera to a Sony
Trinitron PVM-14M2MPU color monitor through an S-video
2) Training: Each subject received specific training on
the system prior to the main study. Each subject watched an
orientation video about the RAVEN surgical robot and how
to perform telemanipulation tasks. The video broke down
manipulation into three parts: (1) positioning, (2) orienting,
and (3) grasping, first with dominant hand, then with non-
dominant hand. The subjects were instructed on three tasks
NUMBER OF REPETITIONS FOR EACH OF THE TRAINING TASKS
(described below) that would enable them to successfully
teleoperate using the RAVEN. During the pilot study ,
each task was performed until the subject’s completion time
for that task did not improve over three trials. Based on
subject feedback as well as analysis of the training data,
a fixed number of repetitions for each of the tasks were
performed for this study (see Table II. Once the subject
had completed a tasks (s)he was allowed to move on to
the next task. The subjects trained until they had completed
all three tasks. The subjects then repeated the same training
tasks under a delay condition of 250ms. By first training
the subjects with no-delay, they were able to learn the
psychomotor skills necessary to telemanipulate objects under
the most ideal conditions. Then, by repeating the training
tasks under a delay condition, they learned to accommodate
for delay. Ideally, in order to reduce subject fatigue, the non-
delay and delay training were completed on separate days.
In practice, due to scheduling this was not always possible.
The training task board was built on a 4” x 2.5” piece
of plexiglass. Six 1” x 1/4”-20 countersink screws were
arranged in a grid of two rows of three. The screws were
capped by 1” pieces of 1/4” inner-diameter rubber tubing and
arranged with 1” spacing between each of the three columns
and 7/8” spacing down between each of the two rows. Each
of the six pegs were numbered 1-6 as shown in Figure 4. The
following list describes the training tasks for the dominant
hand. Tasks 1B, 2B and 3B, the tasks for the non-dominant
hand, are similar.
• Task 1A Dominant Hand Positioning Using the domi-
nant hand’s tool, touch each peg in sequence 1 through
6, while keeping the non-dominant hand’s tool in the
field of view. You will know you’ve touched the peg
when you see it deflect.
• Task 2A Dominant Hand Orientation Using the domi-
nant hand’s tool, orient the grasper tips and place the
tips into the center of each peg in sequence 1 through 6
while keeping the non-dominant hand’s tool in the field
• Task 3A Dominant Hand Grasping Using the dominant
hand’s tool, open the grasper tips, place the tips with one
jaw in the center of each peg and one jaw on the outside
of the peg, then grasp the peg wall. Grasp each peg in
sequence 1 through 6 while keeping the non-dominant
hand’s tool in the field of view. When grasping with
the right tool, grasp the right side of the peg. When
grasping with the left tool, grasp the left side of the
3) Warm-up: If the subject had been away from the
system for more than an hour, they were presented with
Fig. 4.Training Task Board
THREE DIFFERENT CONDITIONS WERE PRESENTED TO EACH SUBJECT
the training task board and were required to perform a 5-
minute warm-up during which they performed the training
tasks. The warm-up was performed with no time delay and
subjects were allowed to move at their own pace.
4) Experimental Design: In this study, three delay con-
ditions were investigated (summarized in Table III): 0ms
(Treatment A), 250ms (Treatment B), and 500ms (Treatment
C). Subjects performed three repetitions of each of the three
treatments for a total of nine trials. The nine trials were
arranged in a pseudo-random fashion. The treatments were
grouped into three bins so that each bin contained one of each
of the three treatments. Six possible bins resulted from the
permutations of the three conditions. When subjects arrived
they performed an urn sampling without replacement of three
numbered balls from a box originally containing six balls.
The order of the balls determined the order of the bins. For
example if the subject drew 3, then 5, then 1, the order of
their nine trials would be (B, A, C), (C, A, B), (A, B, C).
Designing the order so that the subject received one of each
of the treatments before receiving a second (and so on) meant
that, if a learning effect was present during the experiment,
the improvement in performance would be distributed more
evenly across the different treatments.
Between each of the trials, the subjects received a short
break, and after the third and sixth trial, the subjects were
required to take a minimum 5-minute break to help minimize
fatigue. Before each trial the subject was told which treat-
ment they were being given to allow them to prepare their
strategy for accommodating for delay.
5) Stated Objective: The subjects were told that, although
they were being timed, speed should not be their optimizing
factor. They were instructed that the goal of the exercise
was to transfer each block carefully, so that no errors were
made (no dropped blocks) at whatever speed was necessary
to insure successful transfer.
6) Subject Population: Fifteen subjects, six surgeons and
nine non-surgeons, nine male and six female, ages ranging
from 18 to 43, participated in this study under University
of Washington Human Subjects Approval Number 01-825-
E/B07. The subjects performed the training tasks first with
no delay, and then with 250ms delay, in order to learn how
to telemanipulate using the RAVEN. Within one week from
the start of their training, they returned to perform the Time
Delayed Block Transfer experiment.
Of the 15 subjects who started the training, 14 finished.
Completion of the training portion of this experiment was a
prerequisite to move to the Block Transfer task.
B. Block Transfer under Time Delay
Five surgeons and nine non-surgeons completed the main
portion of this experiment for a total of 14 subjects. Each
subject performed three repetitions of each of the three
treatments, for a total of 108 block transfers (36 for each
treatment) with the time for each transfer recorded.
For each subject, the mean block transfer time for each of
the three conditions was calculated. A linear regression was
fit to these times and, in all but one case, was fit with an
R2> 0.969. The slope of the linear fit to block transfer time
versus delay represents the subjects’ sensitivity to delay (how
much their completion time increases with increasing delay)
and the y-intercept represents their estimated performance
in the nominal (no delay) condition. The results are listed in
Errors were defined as a block that was dropped. These
were further classified as dropped blocks that were recov-
ered and dropped blocks that were unrecovered. While an
aggregate score or weighting is not given to each of the
two types of errors, subjects were told that a block that was
not recovered would be considered a “worse” error. Table
V lists the total number of errors from each subject over
three repetitions of each of the three treatments and the total
number of errors for the experiment.
While the TeleRobotic FLS as described in this manuscript
reports Block Transfer times and two types of errors, the
RAVEN inherently is able to capture motion data as well.
Position data of each tool was recorded during the trials. For
each trial the distance traversed by each tool was calculated.
The path length is the sum of the distance traversed by the
left and right tools. Table III-B shows the average path length
for each subject for each of the delay conditions. Path length
data from Subjects 1 and 2 were not properly captured and
cannot be reported.
C. Statistical Analysis
Statistical analysis was performed using R. A two-way
analysis of variance (ANOVA) was used to determine the
significant results. Three analyses were performed separately.
The first used block transfer time as the response variable;
SUBJECT BY SUBJECT MEAN BLOCK TRANSFER TIMES REPORTED IN SECONDS FOR EACH OF THE THREE CONDITIONS. THE MEAN TIMES FOR EACH
SUBJECT WERE FITTED TO A LINEAR REGRESSION. THE DELAY SENSITIVITY IS THE SLOPE OF THE LINEAR FIT. THE R2VALUE ASSOCIATED WITH
THE LINEAR REGRESSION IS ALSO LISTED.
SurgeonTreatment A Treatment BTreatment C
ERRORS FOR EACH OF THE THREE TREATMENTS OVER THREE TRIALS FOR EACH AND THE TOTAL ERRORS OVER ALL NINE TRIALS
the second used the number of error; the third used tool tip
path length as the response variable.
The difference in mean block transfer time between each
of the three treatments (0ms, 250ms, and 500ms delay) is
statistically significant, and is illustrated in Figure 5. The
difference in mean path length between each of the three
treatments is significant as illustrated in Figure 6. While the
stated objective of the task was to minimize errors some
errors occurred but the number of errors in response to
delay effect and surgeon effect was not significant. It is
possible that if the task was more technically challenging,
the frequency or severity of errors would start to differentiate
between subjects with more and less skill. The difference
in mean block transfer between surgeons and non-surgeons
is not statistically significant. The difference in mean path
length between surgeons and non-surgeons is significant at
the 0.05 level.
At the outset, there was a hypothesis that surgeons might
be more careful and therefore less prone to errors. Another
possibility is that surgeons who perform MIS cases would
SUBJECT BY SUBJECT MEAN BLOCK TRANSFER PATH LENGTH REPORTED IN METERS FOR EACH OF THE THREE CONDITIONS. THE MEAN PATH
LENGTH FOR EACH SUBJECT WERE FITTED TO A LINEAR REGRESSION. THE DELAY SENSITIVITY IS THE SLOPE OF THE LINEAR FIT IN MILLIMETERS
OF INCREASED PATH LENGTH PER MILLISECOND OF INCREASED DELAY. THE R2VALUE ASSOCIATED WITH THE LINEAR REGRESSION IS ALSO LISTED.
Surgeon Treatment ATreatment BTreatment C
< 2e − 16
1.978e − 08
be more adapted to the lack of depth perception. It was also
suggested that surgeons would be better at accommodating to
delay. The statistical analysis shows that there is no signifi-
cant difference between surgeons and non-surgeons for these
telerobotic manipulation tasks. Clearly, the ability to perform
surgery extends far beyond the psycho-motor skill of moving
blocks on a pegboard, requires (among many other skills)
a high level of cognitive ability, familiarity with anatomy,
and the ability to deal with unexpected problems. However,
for the purposes of the development of a surgical robot,
these results imply that performing usability or evaluation
experiments with non-surgeons may be adequate when using
simple tasks such as the Block Transfer. Complex tasks such
as suturing, may require surgeon participation. In addition,
the effects of delay may be more dependent on the individual
than on the individual’s surgical expertise.
Fig. 5. Delay Effect versus block transfer time. 0ms average block transfer
time was 36.95sec; 250ms average block transfer time was 53.60sec; 500ms
average block transfer time was 75.44sec.
V. CONCLUSIONS AND FUTURE WORKS
The development of the TeleRobotic FLS tasks has estab-
lished a standardized means by which any group working in
the area surgical robotics can conduct performance testing for
a multitude of hypotheses. The SAGES FLS skills tasks kit
0 250 500
was 7.629m; 250ms average tool tip path length was 9.781m; 500ms average
tool tip path length was 11.724m.
Delay Effect versus path length. 0ms average tool tip path length
can be readily purchased and is already a standard amongst
many surgical residency programs. Standardizing on TeleR-
obotic FLS will benefit surgical robotics researchers much in
the same way it has benefited surgeons - it creates a common
reference frame in which to understand each others results.
Currently, an Interoperable Teleoperation Protocol (ITP) is
being developed between the University of Washington,
SRI International, Tokyo Institute of Technology and other
Universities. An initial ITP experiment with the University of
Washington, and Tokyo Institute of Technology investigates
the performance of two different master devices controlling
the RAVEN using the Block Transfer Task .
The authors gratefully acknowledge technical contribution
from Diana C.W. Friedman and Ganesh Sankaranarayanan
of the UW BioRobotics Lab. The authors would also like
to acknowledge the participants in this study for being
so generous with their time. Funding for the development
of the RAVEN was provided by US Army MRMC grant
number DAMD17-1-0202. Additional support for this work
was provided by Intel Research Seattle.
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