Journal of Robotics, Networking and Artificial Life
Vol. 6(2); September (2019), pp. 113–117
DOI: https://doi.org/10.2991/jrnal.k.190828.009; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Pulse Point Position Tracking-control and Simulation basedon 4-DOF Pulse Diagnosis Robot
Qunpo Liu1,*, Guanghui Liu1, Hongqi Wang1, Xianzhe Liu1, Naohiko Hanajima2
School of Electrical Engineering and Automation, Henan Polytechnic University, 2001 Century Avenue, Jiaozuo, Henan 454003, P. R. China
College of Information and Systems, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran-shi, Hokkaido 050-8585, Japan12ARTICLE INFO
Article History
Received 20 October 2018
Accepted 19 November 2018
Keywords
Pulse diagnosis
robot
follow control
simulation

ABSTRACT
Pulse diagnosis has been proved to be of great practical value in the past dynasties. The key of high-quality pulse diagnosis is how
to dynamically adjust the vertical relationship between the pulse and the sensor dynamically. The design adopts a four degree of
freedom manipulator combined with a matrix sensor to form a diagnostic robot, so as to achieve rapid adjustment and keep thediagnostic pulse sensor perpendicular to the pulse. As a result, kinematics analysis and simulations can be performed and thefeasibility of the results can be showed as well.

© 2019 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
For more than 2000 years, pulse diagnosis has played a very importantrole in the practice of diagnosis and treatment [1]. The theory of pulse
diagnosis has been applied through the physiology, pathology, diagnosis, treatment and other aspects of Chinese medicine. The qualityof pulse diagnosis is related to the effect of “syndrome differentiationand treatment” in all clinical departments, which is of great significance to the guidance of Traditional Chinese Medicine (TCM) theory
in medical practice. Since the 1960s, with the development of modern
science and technology, many domestic and foreign researchers havebeen committed to promoting the modernization of TCM pulse diagnosis and expected to improve the situation of “difficult to identify” byusing modern medical instruments [2].

2. COMPOSITION OF THE PULSEDIAGNOSIS ROBOTThe key of high-quality pulse diagnosis is how to dynamicallyadjust the vertical relationship between the pulse and the sensor(only the vertical relation between sensor and pulse can guarantee
the accuracy of pulse diagnosis). The robot is made up of a Four
Degree of Freedom (4-DOF) manipulator and a pulse diagnosis
sensor matrix, as shown in Figures 1 and 2.

3. KINEMATICS OF THE 4-DOF
MANIPULATORAccording to the standard D–H parameter table method, thecoordinate system of the manipulators is shown in Figure 3.
Corresponding author. Email: lqpny@hpu.edu.cn*

The coordinate system 1 is coinciding with the origin of the basecoordinate system 0, and the origin of the coordinate system of
the pulse sensor coincides with the origin of the wrist coordinatesystem. The D–H parameters are obtained as shown in Table 1.
The length of the connecting rod is ai: the distance from the Zi−1
axis to the Zi axis is moved along the Xi axis. The angle of the connecting rod is ai: the angle of rotation of the Zi−1 axis to the Zi axis
around the Xi axis. Connecting rod offset di: along the Zi−1 axis, thedistance between the Xi−1 axis and the Xi axis is moved. Joint angle
qi: around the Zi−1 axis, rotate the Xi−1 axis to the angle of the Xi axis.

3.1. Forward Kinematics of the Manipulator
According to the standard D–H parameter method, the general equation of the link homogeneous transformation matrix is[Equation (1)]:cq i sq	   T =  0 i 0i −1i− c a i sq i
c a i cq i
sa i0s a i sq i
− s a i cq i
ca i
0ai cq i ai sq i di  (1)1 where, c = cos, s = sin.
From Equation (1) and the established D–H parameter table, thehomogeneous transformation matrix equation of each adjacent
joint can be obtained [Equation (2)]:c1c234
s c
T = T T T T =  1 234
 − s234 00
401122334− c1s234− s1s234− c2340− s1 a3 c1c23 + a2 c1c2 
c1 a3 s1c23 + a2 s1c2 0−a3 s23 − a2 s2  (2)01Q. Liu et al. / Journal of Robotics, Networking and Artificial Life 6(2) 113–117

114

with the algebraic method, the geometric method has advantagesof simple calculation and avoids the discussion of range of values
by using the constraint relationship between the joints [4]. In thispaper, the inverse kinematics of the 4-DOF pulse diagnosis robot issolved by geometric method [5].
Before solving the kinematics with the geometric method, the firstrule is that q1 has rotated to a position parallel to the target position.
According to the establishment rule of D–H, the schematic diagram of the plane based on the geometric method can be obtained
as shown in Figure 4.
Figure 1 | The robot which is made up of a 4-DOF manipulator and apulse diagnosis sensor matrix.

Assuming that the position coordinates (xp, yp) of the end point P
and the azimuth angle γ of the point P are known. According to thegeometric relation, Equations (3) and (4) can be drawn that:xo 3 = x p - a4 cosg (3)		  yo 3 = y p - a4 sing (4)		  
By using the cosine theorem in Δo1o2o3, Equations (5), (6) and (7)can be drawn that:	   q 3 =(arccos xo23 + yo23 − a22 − a32
2a2a3
2o3

(5)

2223( ) x 2a+ y x + +a y+ a (6)yFigure 2 | The pulse diagnosis matrix sensor is composed of five pressure
sensors in a row. The matrix sensors are fitted on the end of the manipulators.

2o3)

o3	   q 2 = arctan xo 3 ±22o32o3	   q = g − q − q (7)423

4. MODELING AND SIMULATION
4.1. Modeling in V-REP
The robot model is established in Virtual Robot Experimentation
Platform (V-REP) as shown in Figure 1. Setting the parameters ofthe robot model: a2 = 0.08 m, a3 = 0.1 m, a1 = −90°. The initial posture of the robot can be calculated by assigning the angle of eachjoint to 0° as shown in Equation (8):Figure 3 | Manipulators structure and its coordinate systems of joints.
Table 1 | Parameters of D–Hi

`iai

dipi

1234−p/20000
a2
a300000

q1q2q3q4

1 0

0 00		 4Tinitial position = 0 −1
0 00 0.18
10 
00  (8)01 This data we got is identical to the data obtained in the V-REP
simulation.

where, c234 = cos (q2 + q3 + q4), and the others can be replaced in
the same way.

3.2. Inverse Kinematics of the Manipulator
The common methods of inverse kinematics solving mainly
include algebraic method and geometric method [3]. Compared

Figure 4 | Geometric relationship of three joints in XY-plane.

Q. Liu et al. / Journal of Robotics, Networking and Artificial Life 6(2) 113–117

115

The pulse diagnosis matrix sensor adopted in this simulationis composed of five pressure sensors in a row, which is shown in
Figure 5a. Each point of the sensors can sense the corresponding
pulse. In the simulation, the pulse sensor is exposed to the pulse atdifferent positions to obtain the simulation data. By analyzing thedata, the relative pose of sensors with respect to the pulse can bedrawn, which will be used to adjust the posture of the joints.
Pulse model is also established with similar dynamic properties
to the human pulse, as shown in Figure 5b. The width of the pulse
model is set as 3.6 × 3.6 mm2 according to average adult’s size.

4.2. Pulse Detect Simulation
In the V-REP simulation, the pulse beat curve at different positions is measured with changing the relative position of the pulse
matrix sensor relative to the pulse model. When the first sensor ofthe pulse matrix sensor is aligned with the pulse model, the pulse
curve obtained is shown in Figure 6. It is quite obvious from thefigure that the value of sensor 1 is the largest, and the center of thepulse matrix sensor is not perpendicular to the pulse model.

Figure 7 | Beating curves of sensors when the third sensor is aligned withthe pulse.

When the third sensor of the pulse matrix sensor is aligned withthe pulse model, the pulse curve is shown in Figure 7. It is clearthat the intensity of sensors 2–4 are same to each other. When thecurve similar to Figure 8 is obtained, the pulse matrix sensor canbe considered as perpendicular to the pulse, and the third sensor isaligned with the pulse.

4.3. Pulse pose tracking control simulation

Figure 8 | Curve change of each sensor point during pulse pose trackingcontrol.

Figure 8 shows that the posture curves of the pulse sensors. The
values changes related to the relative position between sensors andthe pulse, until the matrix sensor is perpendicular to the pulse, andabFigure 5 | Pulse sensor and pulse model in V-REP. (a) Pulse sensor model.
(b) Pulse model.

Figure 9 | Curve change of q2, q3, and q4 during pulse pose followingcontrol.

Figure 6 | The first sensor is aligned with the pulse.

sensor 3 is aligned with the pulse. Figure 9 shows that the joints
were adjusted to move from sensor 5 to sensor 3, and finally sensor 3
is perpendicular to the pulse as expected.

Q. Liu et al. / Journal of Robotics, Networking and Artificial Life 6(2) 113–117

116

5. CONCLUSION
In this paper, a diagnostic robot with a 4-DOF manipulator anda pulse diagnosis matrix sensor was introduced. The kinematic
was analyzed and the model was simulated on V-REP. The simulation results show that the proposed robot can find the position of pulse and adjust the joints to the posture as expectedaccording to the feedback information from the pulse diagnosis
matrix sensor.

of Henan Provincial High Education (20IRTSTHN019), and theInnovative Scientists and Technicians Team of Henan Polytechnic
University (T2019-2).

REFERENCES[1][2]

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

[3]ACKNOWLEDGMENT[4]

This work is partially supported by Innovation Scientists andTechnicians Troop Construction Projects of Henan Province (GrantNo. CXTD2016054), Innovative Scientists and Technicians Team

[5]

J. Shu, Y. Sun, Developing classification indices for Chinese pulse
diagnosis, J. Com. Ther. Med. 15 (2007), 190–198.
H. Chang, J.X. Chen, Piezoelectric pulse diagnosis transducerof 9×9 sensing arrays and pulse signal processing, International
Conference on Applied Informatics and Communication, Berlin,
Heidelberg: Springer-Verlag; 2011, pp. 541–548.
A. Ghosal, Manipulator kinematics, in: A. Nee, (eds.), Handbookof manufacturing engineering and technology. London, Springer,
2015, pp. 1777–1808.
T. Bajd, M. Mihelj, M. Munih, Introduction to Robotics, Springer,Dordrecht, 2013.
A. Phunopas, E. Hayashi, A Four-legged, Robot’s soft feet structural design and walking gait generated from inverse kinematics,
J. Rob. Net. Art. Life 5 (2018), 161–164.

Authors Introduction
Dr. Qunpo Liu

Dr. Hongqi Wang
He graduated from the MuroranInstitute of Technology (Japan) witha PhD in Production Information
Systems. He is an Associate Professor atthe School of Electrical Engineering at
Henan Polytechnic University (China)
and a master’s tutor. He is mainly
engaged in teaching and research workin special environment mobile robots,intelligent instruments and industrial
automation equipment.

Mr. Guanghui Liu

He is an Associate Professor at theSchool of Electrical Engineering,
HenanPolytechnicUniversity(China), as well as a tutor for postgraduate students. His main research
direction is robot control.

Mr. Xianzhe Liu
Hegraduatedfrom
Henan
Polytechnic University (China) in2018 with a bachelor’s degree inautomation. He is currently studying for a master’s degree in Control
Engineering at Henan Polytechnic
University. He is mainly engaged inresearch on pulse diagnosis robot.

He is currently studying for a bachelor’s degree in automation at HenanPolytechnic University (China). He ismainly engaged in modeling and simulation of robot

Q. Liu et al. / Journal of Robotics, Networking and Artificial Life 6(2) 113–117

Dr. Naohiko Hanajima
He graduated from the Hokkaido
University (Japan) of Technology inJapan with a PhD. He is a Professor
at the College of Information andSystems at Muroran Institute ofTechnology (Japan). He is mainly
engaged in robotics and intelligent
equipment.

117