Robotic Stroke Motion Following the Shape ofthe Human Back: Motion Generation andPsychological Effects
Akishige Yuguchi

Tomoki Ishikura

Sung-Gwi Cho

Faculty of Advanced Engineering
Tokyo University of Science
Katsushika-ku, Tokyo, Japanakishige.yuguchi@rs.tus.ac.jp

Division of Information Science
Nara Institute of Science and Technology
Ikoma, Nara, Japan

School of Science and Engineering
Tokyo Denki University
Hiki-gun, Saitama, Japans.g.cho@mail.dendai.ac.jp

Jun Takamatsu

Tsukasa Ogasawara

Applied Robotics, Microsoft
Redmond, WA, USA
jun.takamatsu@microsoft.com

Nara Institute of Science and Technology
Ikoma, Nara, Japanogasawar@is.naist.jp

I. I NTRODUCTION
Several caregiving techniques such as Tactile Massage [1]and Humanitude [2] require skillful or therapeutic touch
through physical interaction. Furthermore, several studies havereported that touching and stroking patients with such skillse.g., massage produces several kinds of positive effects such asa reduction in pain [3] and improvements in sleep quality [4].
On the other hand, the lack of caregivers and the labor shortagein those skills have been increasing, hence, it is expected thatrobot arms instead of human hands perform massage1 anddementia care through skillful stroke touch [5].
To address those expectations, a few studies elucidated thatstroking the human back using a robot arm evokes pleasantfeelings in participants similar to those evoked by humans [6],[7]. While the robotic stroke motions on the back were
implemented with a linear trajectory for vertically stroking theback in these studies, the motion appearance was far from thatof the stroke motions by humans because the human hand canfollow the shape of the human back by stroking. We thoughtthat it remains unclear whether the robot arm can generatestroke motions following the shape of the human back as
well as humans do and whether the robotic stroke motions
following the shape of the back evoke pleasant feelings inparticipants similar to those evoked by humans.
In this study, we propose a method to generate stroke
motions using a robot arm to follow the shape of the humanback. The method generates a trajectory to follow the shapeof the human back for a robotic stroke motion with a cubic
function to model the shape of the human back from the depth
image obtained by a depth camera. We confirm whether theThis work was supported by JST CREST Number JPMJCR17A5, Japan,
and conducted mainly at the Division of Information Science, Nara Instituteof Science and Technology, Japan.
1 Aescape Inc., https://www.aescape.com/

generated trajectories can be similar to the trajectories of thestroke motions on the back by humans. Finally, we evaluate thepsychological effect on the participants’ backs by comparing
the robotic stroke motion following the shape of the humanback with the proposed method and the conventional stroke
motion with a linear trajectory.
II. T RAJECTORY G ENERATION FOR ROBOTIC S TROKEM OTION F OLLOWING THE S HAPE OF THE H UMAN BACKThe stroke motion in this study refers to a stroke motion in a
fixed vertical direction from the upper human back to the lower
human back, as in the aforementioned previous studies [6],[7]. To generate a trajectory of the stroke motion following
the shape of the human back, we use a depth camera withcalibrated internal parameters to recognize the shape of theback in the stroking direction by calculating the 3D positionin actual space from a depth image. Specifically, first, weselect the start and end positions of the stroke motion fromthe obtained depth image, connect the start and end positionsof the stroke motion by a straight line, and then store theobtained depth values as z in the section between the startand end positions from 3D positions p(xi , yi , zi ) in the camera
coordinate system ΣC . x is the horizontal direction of thecaptured depth image (the width) and y is the vertical directionof the captured image (the height). In this case, since thestroking direction is constrained only in the vertical direction(on the y axis), we set the x values at the start and end
positions to be the same in the image.
Next, we generate a trajectory for the stroke motion using arobot arm from the recognized shape of the human back withthe following four steps:
1) Convert the recognized shape of the human back toapproximate a cubic curvehow much the normal directions match from the normal
vector B of the back and the normal vector E of the endeffector by (2).

Start point

θerrorx = cos−1

B·E.
|B||E|(2)

Finish point

A. Camera image.

B. Depth image.

Fig. 1: Actual camera and depth images of the back.

2) Calculate the waypoint positions for the trajectory of thestroke motion from the approximated curve3) Calculate the target angle for the orientation control ofthe end-effector
4) Transform the waypoint positions from the camera coordinate system ΣC to the robot coordinate system ΣR
First, we convert the recognized shape of the back by approximating a cubic function (z = ay 3 +by 2 +cy +d) into a smooth
curve. Second, we calculate the corresponding depths of theheight y in increments of 1.0 [mm] from the approximate
curve and then set those points as the waypoints pway (x, yi , zi )through which the end-effector moves. Third, we calculate thetarget angle θtargetx by (1) to control the orientation of the endeffector in the x-axis direction.
zi − zi−1(1)θtargetx = tan−1
yi − yi−1
Finally, we transform the waypoint positions pway (x, yi , zi )from the camera coordinate system ΣC to the robot coordinate
system ΣR . We input the generated waypoints pway (x, yi , zi )and the target orientation θtargetx of the end-effector into the
motion planning tool Moveit2 to generate the stroke motion.
III. G ENERATION OF ROBOTIC S TROKE M OTION AND I TS
T RAJECTORY E VALUATION
A. Trajectory and Motion Generation
We used a cooperative robot arm UR3e produced by Universal Robots similar to those used in the previous studies [6],[7]. We attached a depth camera Intel RealSense D435 near
the robot base. To generate the stroke motions on the back,we used a mannequin dressed in patient wear3 .
In the actual shape recognition of the human back, we took
a depth image of the back and then selected the start andend positions of the stroke motion. Then, we generated the
trajectory and the stroke motion using the proposed method.
Figure 1 shows the actual recognized shape of the back andthe start and end positions.
B. Evaluation
To evaluate the generated trajectory of the robotic stroke
motion following the shape of the human back, we calculated

We measured the generated motion trajectory five times using
an optical motion capture system OptiTrack4 . The normal
vector B was the vertical direction on the plane of the rigid
body created from the markers on the back. We attached fivepairs of the markers at 5.0 [cm] increments from the upperback of the patient wear worn by the mannequin, 20.0 [cm]in total. The normal vector E was the vertical direction on
the plane of the rigid body created from the markers on theend-effector. The trajectory range of the stroke motion was
defined from the uppermost markers on the upper back to
passing through the lowest markers.
Figure 2 shows the actual generated motion using the robot
arm, the back displayed on the motion capture system, andthe transition of the normal vector E of the end-effector. The
average error θ̄errorx at each point of the motion trajectories
was 5.97 [deg] and the maximum error max θerrorx was 8.26
[deg].
IV. C OMPARISON WITH S TROKE M OTION T RAJECTORY TO
THE BACK BY H UMANS
A. Measurement of Stroke Motions by Humans
We measured stroke motions on the back by humans tocompare the trajectories of the robotic stroke motions following the back with those of the motions by humans.
For the stroke motions by humans, we measured thedemonstration of the motion by two performers who stroked
the participants’ backs in the previous studies [6], [7]. We
measured the vertical direction on the plane of the rigid body
created from the markers attached to the tip of the performer’s
right hand as the normal vector H of the hand. Then, we
evaluated how similar the vector H replaced from the normal
vector E in (2) is to the normal vector B of the back. In
addition to the measurement in Section III, we measured the
motions on the back of a mannequin wearing the patient wearwith the markers in the same posture five times, starting fromthe uppermost markers on the upper back to passing throughthe lowest markers.
B. Measurement Result
Figure 3 shows the actual human demonstration of the stroke
motion, the back displayed on the motion capture system, andthe transition of the normal vector H of the hand.
The average error θ̄errorx from each point on all the motion
trajectories was 4.78 and 5.52 [deg] for the first and second
performers, respectively, and the maximum error max θerrorx
was 7.30 and 8.13 [deg] for the first and second performers, respectively. These results indicated that the average
error θ̄errorx and the maximum error max θerrorx of the robotic

2 MoveIt, https://moveit.ros.org
3 SG1441, Nagai Leben, Inc.

4 OptiTrack, http://www.optitrack.com

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 2: The actual robotic stroke motion and the measured normals on the back and robot’s end effector.
(a)

(b)

(c)

(d)

(e)

(f)

Fig. 3: The actual human stroke motion and the measured normals on the back and hand.

stroke motion in Section III were 5.97 [deg] and 8.26 [deg],
which were close to those of the stroke motions by humans.
Therefore, we deemed that the robotic stroke motion using the
proposed method could follow the shape of the back.
V. S UBJECTIVE E XPERIMENT
A. Method
To evaluate the effect of the stroke motion following theshape of the human back using the actual robot arm, we
attached the human-mimetic robotic hand used in the previous
studies [6], [7] to the end-effector, and compared the psychological effect of the stroke motion with the proposed method
and the conventional stroke motion with a linear trajectory
used in the previous studies by subjective evaluation.
From the previous studies, we used the Affect Grid [8]for subjective evaluation. Affect Grid consists of 9 × 9 two-

dimensional grids with a horizontal and vertical axis, each
representing a 9-point scale of valence (pleasant-unpleasant)and arousal level (high-low). The left and right directionsin the horizontal axis correspond to pleasant and unpleasantfeelings (valence), and the up and down directions in thevertical axis correspond to high and low arousal.
We prepared totally four conditions: two types of motions, i.e., the stroke motion following the shape of the humanback (the proposed method) and the stroke motion with alinear trajectory (the conventional method used in the previous
studies), and two types of speeds (slow: 2.8 [cm/s] andmedium: 8.5 [cm/s]) similar to the experiments in the previous
studies.
To control the experimental environment, we asked participants to wear patient wear and earmuffs as in the previous
studies. Furthermore, to reduce the participants’ anxiety, weSlowMedium･･

A. Valence.
n.s.

*

*

n.s.

B. Arousal.
Fig. 4: The actual experimental setup

asked the participants to observe only the approach to the backbefore the start of the robotic stroke motion through a screen
placed in front of the chair for the participants.
In the experimental procedure, first, we explained the experimental procedure to a participant and then asked him towear the patient wear. Second, we took an image of the shapeof the participant’s back with the depth camera and asked him
to move as little as possible during the experiment. The shapeof the back was recognized only once before the start of theexperiment, and the stroke motion following the shape as thecondition was generated from this shape recognition result.
Third, we conducted a pretest that the robot arm generated thestroke motion following the participant’s back at low speed toreduce the participant’s anxiety about the robot arm and to
confirm the visibility of the approach. After the pretest, werepeated the following four steps 20 times.
1) We randomly select one of the four conditions, combining the speed (low and medium speed) and the motions(the stroke motion following the shape of the humanback and the stroke motion with a linear trajectory).
2) The robot arm approaches the participant’s back while
showing the approach on the participant’s front screen.
3) We hide the video on the front screen and the stroke
motion is performed for about 20 seconds.
4) After the stroke motion, the participant intuitively andsubjectively evaluates the feeling by the stroke motion
in about 10 seconds.
B. ResultThis experiment was approved by the Ethics Committee ofNara Institute of Science and Technology, Japan. Five healthy
Japanese adult males (mean ± SD age, 23.4 ± 0.8 years)
participated in the experiment after informed consent was
obtained from all participants.
Figure 5 shows the results of the Affect Grid. Figure 5-A
shows the valence scores (pleasant - unpleasant), with higherscores indicating pleasantness and lower scores indicating
unpleasantness. The left and right figures in Figure 5-A show

Fig. 5: The result of subjective evaluation using Affect Grid(∗: p < 0.05). Note that the condition non-linear trajectory is
the stroke motion following the shape of the human back withthe proposed method.

the valence scores for each condition and the two factors(speed and motion) for each level (speed: low and medium
speed, motion type), respectively. Figure 5-B shows the arousal
scores (high - low), with higher scores indicating higher
arousal and lower scores indicating lower arousal.
In the valence scores, Figure 5-A shows that the stroke
motion following the shape of the human back evoked more
pleasantness than the conventional stroke motion with a linear
trajectory, and the difference in speed was similar to thatin the previous studies. An ANOVA analysis was conducted
to evaluate the difference between the two motion types,
and no interaction was found: F (1, 4) = 0.652, p > 0.1.
Furthermore, a main effect test showed marginal significancewith F (1, 4) = 7.236, p < 0.1 and F (1, 4) = 5.548, p < 0.1
for the types of motion and speed, respectively.
In the arousal scores, Figure 5-B shows that the stroke
motion following the shape of the human back evoked higher
arousal than the conventional stroke motion with a linear
trajectory, and the difference in speed was similar to thatin the previous studies. An ANOVA analysis revealed aninteraction effect of F (1, 4) = 15.844, p < 0.5. Furthermore,a simple main effect test was conducted as a post-hoc test,
and no significant difference was found in comparing the lowspeed motions, F (1, 4) = 0.004, p > 0.1. The comparisonof the medium-speed motions was significantly different withF (1, 4) = 6.382, p < 0.05.
The speed comparison of the stroke motions following theshape of the human back showed a significant difference,
F (1, 4) = 7.851, p < 0.05, indicating that the medium-speed
stroke motion following the shape of the back evoked higher
arousal than the low-speed stroke motion following the shapeof the back. On the other hand, no significant difference wasfound in the speed comparison of the stroke motions with alinear trajectory, F (1, 4) = 1.198, p > 0.1.

C. Discussion
In terms of valence, the stroke motion following the shapeof the human back tended to evoke more pleasantness thanthe conventional stroke motion with a linear trajectory. On
the speed aspect, it was shown that the medium-speed stroke
motion tended to evoke more pleasant than the low-speed
stroke motion, similar to the results in the previous studies.
In terms of arousal, the stroke motion following the shape ofthe human back tended to evoke higher arousal than conventional stroke motion with a linear trajectory. In particular, themedium-speed stroke motion following the shape of the backwas higher arousing than the medium-speed stroke motion
with a linear trajectory. On the speed aspect, it was shown thatthe medium-speed stroke motion following the shape of theback evoked higher arousal than the low-speed stroke motion
following the shape of the back, similar to the previous studies.
These results indicated that the stroke motion following theshape of the human back with the proposed method tended
to evoke pleasant and higher arousal, i.e., more pleasant andactive feelings, than the conventional stroke motion with alinear trajectory, and that the medium-speed stroke motion
especially evoked higher arousal, i.e., more active feelings. The
differences in the speed of the stroke motions were similar to
those in the previous studies, confirming the previous findings.
The results suggested that the stroke motion following theshape of the human back with the proposed method tended
to have a certain positive effect compared to the conventional
stroke motion with a linear trajectory. Thus, we expect thatthese human-like therapeutic stroke motions on the back could
be implemented by robots instead of human skills to perform
massage and dementia care.
On the other hand, note that this study has a few possible
limitations. First, we only used a cubic function to generatea trajectory of the robotic stroke motions following the shapeof the human back. The actual shape of the human back isalways not fixed e.g., when a human breathes. Hence, realtime generation of trajectories of the robotic stroke motions
is needed. We consider that there is a case where the only
use of a cubic function may be about to fail the target
trajectory generation. Second, as referred to in Section II, thestroke direction on the human back is constrained. In actual
situations of care and massage, we consider that the stroke
direction can be various. Third, the number of participantsin the subjective experiment was very limited. To confirm thegeneral psychological effects, we need to ask more participants
to have experiences with the stroke motion with the proposed
method.
VI. C ONCLUSION
In this study, to perform the robotic stroke motions following the shape of the human back similar to the stroke
motions by humans, in contrast to the conventional robotic
stroke motion with a linear trajectory, we proposed a trajectory
generation method for a robotic stroke motion following theshape of the human back. We confirmed that the accuracy ofthe method’s trajectory was close to that of the actual stroking

motion by a human. Furthermore, we conducted a subjective
experiment to evaluate the psychological effects of the proposed stroke motion in contrast to those of the conventional
stroke motion with a linear trajectory. The experimental results
showed that the actual stroke motion following the shape of thehuman back tended to evoke more pleasant and active feelings
than the conventional stroke motion.
In future work, we would like to increase the number ofparticipants and further evaluate the psychological effects of
the robotic stroke motion following the shape of the humanback. In addition, we would like to extend our method togenerate robotic stroke motions following the shapes of otherparts of the body.
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