https://doi.org/10.20965/jaciii.2019.p0519
Human Posture Recognition for Estimation of Human Body Condition

Paper:

Human Posture Recognition for
Estimation of Human Body Condition
Wei Quan∗ , Jinseok Woo∗, Yuichiro Toda∗∗ , and Naoyuki Kubota∗∗ Graduate School of Systems Design, Tokyo Metropolitan University

6-6 Asahigaoka, Hino, Tokyo 191-0055, Japan
E-mail: quan-wei1@ed.tmu.ac.jp, {woojs, kubota}@tmu.ac.jp∗∗ Graduate School of Natural Science and Technology, Okayama University
3-1-1 Tsushima-Naka, Kita, Okayama, Okayama 700-8530, Japan
E-mail: ytoda@okayama-u.ac.jp[Received November 30, 2018; accepted December 25, 2018]

Human posture recognition has been a popular research topic since the development of the referent
fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment. In
this study, we propose a solution to this by applying a
number of unsupervised learning algorithms based on
the forward kinematics model of the human skeleton.
Next, we optimize the proposed method by integrating particle swarm optimization (PSO) for optimization. The advantage of the proposed method is no pretraining data is that required for human posture generation and recognition. We validate the method byconducting a series of experiments with human subjects.

Keywords: human posture recognition, growing neuralgas, particle swarm optimization, human-robot interaction

1. Introduction
Human posture recognition has been a popular research
topic ever since first computer computational recognition method appeared, and many state-of-the-art methods
have been proposed by researchers and engineers aroundthe world. It has been applied to various areas, such as
human-robot interaction [1–3], operating simulations, andgames development.
Human-robot interaction might be the area with themost applications of posture recognition. It makes it possible for humans to communicate with robots not only
though cold commands inputted from the keyboard, butalso though gesture language understood by computers [1,4].
At the same time, with the increase of the number of
elderly people all over the world, health care issues are
getting more and more important. To solve these issues,
human motion capture systems are required for the elders
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who live alone. And elders’ state of health can be monitored by determining his or her posture, and an alert canbe given in the case that high risk postures, such as falldown, are detected. These series of systems will reducethe burden of human resources while improving the efficiency of posture recognition [5].
Previous human skeleton recognition research was conducted using standard pin-hole digital cameras. However,
it is impossible to detect a human’s real spatial postureduring a period of time owning to the natural weaknessof the pinhole camera. The recently developed electronic
devices, such as the RGBD camera, stereo camera, anddepth sensors, have made it possible to capture objects ina real-world shape. In recent years, the most widely used
sensors in the referent field are depth cameras.
In general, depth cameras are divided into two types:
stereo cameras and time of flight (TOF) cameras. Stereo
cameras capture the detail of depth based on the binocular stereo vision theorem [6, 7], which calculates the distance of points in the real world by the principle of parallax and applies two images for measuring the targets thatare captured by two parallel cameras from different positions. The method calculates the positional deviation between the corresponding points of the image for obtaining
the three-dimensional geometric information of an object.
By combining the two images and observing the differences between them, it is possible to obtain a clear senseof depth, establish the correspondence between the features, and map the same spatial physical point in different
images.
By contrast, TOF cameras, also refferred to flight timecameras, obtain the targets’ distance by continuouslytransmitting light pulses to the targets and receiving thelight returned from the object. Next, the camera measures
the flight (round-trip) time of the light pulse [8–10]. This
technique is essentially similar to the principle of a 3D
laser sensor; however, while the 3D laser sensor on; yscans the target point-by-point, the TOF cameras also obtain the information of flight time from a 2D area.
In this paper, we propose a framework for recognizing
human postures by simulating the human body skeleton
and its movements according to the 3-dimensional points

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cloud data using a series of unsupervised learning algorithms.
This paper is organized as follows. In Section 2, we
review several state-of-the-art methods for human bodyrecognition. In Section 3, we explain the details of the
proposed method, and in Section 4, we discuss the experimental results. Finally, concluding remarks are made in
the last section.

3. Proposed Method
In this paper, we propose a human posture recognition
method based on the concept of unsupervised learning.
The framework of this method is shown in Fig. 1. The
learning process can be generally divided into three steps:
preprocessing, growing neural gas (GNG) based roughstructure generation, and parameters optimization by particle swarm optimization (PSO). Detail of each step will
be given in the following section.

2. Related Works
Researchers have been working on the referent field fora long time [11–13]. Consequently, various methods havebeen proposed. In this section, we give a brief review
for several typical methods which have been widely accepted and implemented. In [14], Aggarwal developed ataxonomy that divided all the proposed methods into two
main groups: single-layer approaches and hierarchical approaches.
In [15], the authors proposed the supervised pose
recognition method, which can be considered the mostpopular method today. The process described in the paper is mainly divided into two steps: first, the body partis marked from a single depth image for segmenting, andthen the key nodes are marked. The body joint positioning is then performed, and the marked human body parts
are remapped into the three-dimensional space to form ahighly reliable spatial position for the key nodes.
The authors in [16] proposed a method for human fullbody pose estimation from depth data that can be obtained using TOF cameras and the Kinect device. Theirapproach consists of robustly detecting anatomical landmarks in the 3D data and fitting a skeleton body modelusing constrained inverse kinematics. Instead of relying on appearance-based features for interest point detection, which can vary strongly with illumination and posechanges, they built a graph-based representation of thedepth data to measure geodesic distances between bodyparts. As these distances do not change with body movement, it is able to localize anatomical landmarks independently of the pose.
Recently, in [17], the authors proposed a vector-shapedpose descriptor, which allows for the retrieval of similarposes and is easier to use with many machine learning
libraries by constructing a feature space for appearancesof human poses. This method has improved the limited
scope of many methods based on a kinematic or surface
mesh model, and performed efficiently in experiment.
Nevertheless, the methods mentioned above are basedon supervised learning algorithms, which require a large
amount of data for training before they are applied. Differently from the concept of these methods, in this paper,
we propose a method for human posture recognition by aseries of unsupervised algorithms, which does not require
collection of training data.

3.1. GNG for Human Structure Construction
The particle points cloud data is computationally costly
without peprocessing the data. The GNG is used for representing the points cloud data to a lower density structure, thus we utilized it for the construction of humans’rough structure.
The GNG is a typical self-organizing map (SOM) algorithms for unsupervised learning. It is known that unsupervised learning algorithms are a series of learning
methods that work without any prior input data for training and give the desired output. Input data are consecutively represented by SOM in the form of input signals
and the SOM changes its topological structure for representing the input data with the self-adaptation mechanism.
Next, a growing mechanism is used for gradual adaptation and self-adjustment of size. The growing neural network starts in some minimal state (e.g., with some minimal number of neurons in the network), which is adapted
to the input data. Then, it continually grows (increases
its size) and adapts again. This cycle is repeated until thedesired resolution of the neural network is achieved.
In the GNG learning algorithm, the following notations
are used:wi : n dimensional vector of a node (wi ∈ Rn)
G: set of nodes
Ni : set of nodes connected to the i-th node
c: set of edges
ai, j : age of the edge between the i-th and the j-th node
The steps of the standard GNG algorithm are as follows:
Step 0. Initialize the network by creating two nodes at
random positions, wc1 and wc2 in Rn. Then, set
the connection between them.
Step 1. Randomly generate an input data v according toselecting function p(v), which is the probability
density function of data v.
Step 2. Select the nearest unit (winner) g1 and thesecond-nearest unit g2 by:
g1 = argmin  v − wi  . . . . . . . . (1)i∈G

g2 = argmin  v − wi  . . . . . . . . (2)i∈G/g1

Step 3. Generate the connection a connection between
g1 and g2 , is such connection does not exist al520

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Human Posture Recognition for Estimation of Human Body Condition

Fig. 1. Framework of proposed method.

ready. Set the age of the connection between g1
and g2 to zero:

Add a new unit r to the network and interpolate
its reference vector from q and f :ag1 ,g2 = 0 . . . . . . . . . . . . . (3)Wr = 0.5 · (wq + w f )Step 4. Add the squared distance between the input data
and the winner to a local error variable:
Eg1 ← Eg1 +  v − g2 2. . . . . . . (4)Step 5. Update the reference vectors of the winner andits direct topological neighbors by the learning
rate η1 and η2 respectively, of the total distanceto the input data:. . . . . . . . (8)Create a new edge that connects the new unit rwith units q and f , and remove the existing edge
between q and f .
Decrease the error variables of q and f by a fraction α :Eq ← Eq − α Eq

. . . . . . . . . . (9)E f ← E f − αE f

. . . . . . . . . . (10)wg1 ← wg1 + η1 · (v − wg1 ) . . . . . . (5)Interpolate the error variable of r from q and f :w j ← w j + η2 · (v − w j )Er = 0.1 · (Eq + E f ) . . . . . . . . . (11)if cg1, j = 1(6)

Step 6. Increment the age of all edges emanating froms1 :ag1, j ← ag1, j + 1

ifcg1, j = 1. . . (7)

Step 7. Remove edges with an age larger than a predefined threshold. If this results in units having
no more emanating edges, remove those units as
well.
Step 8. If the error Eq is higher than the predefined
threshold, insert a new unit as follows:
Select the unit f with the maximum accumulated
error among the neighbors of q.
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Step 9. Decrease the error variables of all units:
Ei ← Ei − β Ei(∀i ∈ G). . . . . . (12)Step 10. Continue with Step 1 if a stopping criterion (e.g.,
net size or some performance measure) is not yet
fulfilled.
The number of point could be reduced largely fromthe original point cloud. Therefore the computation time
would also be cut down.
Nevertheless, the standard GNG does not perform in
dynamic environments. Considering this, an improved

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GNG, named GNG with utility (GNG-U) was developed
by Fritzke [18]. It is only slightly the standard GNG inthat it updates not only the local errors but also the utility
Ug1 with
Ug1 ← Ug1 + Eg2 − Eg1 . . . . . . . . . . (13)Then, it removes the node gi if the following inequality
is satisfied:Egi> γ . . . . . . . . . . . . . . . (14)Ugiwhere γ is a parameter that controls the number of nodes.
GNG-U can perform dynamic distributions. Based on
this, Toda [19] proposed the modified GNG-U (GNG-U2)
by introducing the weight vector and has achieved the superior results in 3D structures.

3.2. Human Skeleton ModelingIn this paper, we utilized a simplified kinematic model
for representing the human skeleton. The i-th skeleton isconstructed as m ∈ M joints (M = 15 in this paper):Ji = { jik } = {center shoulder, le f t shoulder,right shoulder, le f t elbow, le f t hand,right elbow, right nand, center torso,
center hip, le f t hip, le f t knee, le f t f oot,right hip, right knee, right f oot}

(15)

The positions of joints are shown in Fig. 2. Each joint
in jik ∈ Ji is represented by 3D coordinates (x jk , y jk , x jk ).
iii
Subsequently the length of the links between eachjoints is described as:
Li = {l1 , l2 , . . . , ln }. . . . . . . . . . . (16)and the spatial coordinates of the skeleton are represented
asFig. 2. Indices of human body’s joints and links.

ing to the fitness function.
To compute the fitness of the skeleton model, we introduced the joints of ribs for auxiliary calculation, eventhough these rib joints have no meanings and can be hidden for human representation.
Because edged nodes contain much more information
than the nodes in the center, as they have more probabilities of representing the limbs of the human, it is necessary
to give them a higher weight than other nodes.
To weight them, first we search for their geographic
center.
Given a weighted skeleton model, our target is to findthe optimized parameters of rotation angles to obtain thefitness skeleton. The proposed method is divided into twoparts, i.e., the initial step and the prediction step.

Pi = {xi , yi , zi } . . . . . . . . . . . . . (17)Despite being more convenient than the previous modelin terms of computation, it is still unstable for generating
the same skeleton for the same person in different frames.
This is because there are two parameters in a skeleton
model: the angles and length of each pair of joints. We
know that the angles of the joints change dynamicallywhen changing posture, but the length of each pair ofjoints is always a constant for one person.
Thus, we try to make the preliminary experiment ofevaluating the length and angels separately. In this model,
there are 16 joints for controlling human posture. We do
not calculate the positions of these joints directly. Instead,
we generate the lengths and angles for each part as shownin Fig. 2 (initial step), and we then calculate the positionof each joints by forward kinematics.
The first part of initialization will solve the parametersof lengths. Given a special posture (e.g., T posture, as
shown in Fig. 2), we randomly generate different values,
and then choose the best one (because the angles are fixed
owing to the fixed posture). In the second part, it randomly generates angles and selects the fittest one accord522

3.3. Denavit-Hartenberg Parameters for Human
Skeleton Modeling
Even though the number of nodes is limited, the construction of the skeleton can to achieved. It is difficult to
directly model the skeleton. Therefore, we applied a moreconvenient way for the representation of skeleton parameters.
The Denavit-Hartenberg (DH) parameters representation is the most widely used in kinematics today. It is
named after Jacques Denavit and Richard Hartenberg who
introduced this representation in 1955 [20, 21]. It calculates the coordinate transformation frame by frame making a list of parameters, with four parameters for eachtransformation:
1. Rotation angle α about X axis
2. Translation a along X axis
3. Translation d along Z axis
4. Rotation angle θ along Z axis.
Because all of the coordinate systems satisfy the constraint, all of the transformation can be represented by a

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Table 1. DH parameters of the human body’s joints.
Position

Joint ID
3

Left arm

4
5
6

Right arm

7
8

Head

2

Torso

9
10

Left leg11
12
13

Right leg

14
15

Index
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

α
0
θ1
θ2 + 90◦θ3
θ40
θ5 + 90◦0θ7θ8θ9 − 90◦θ10θ11 − 90◦θ12−90◦
θ13 − 90◦
0
θ15
θ16
90◦
θ17 − 90◦
0
θ19
θ20

θ
0
0
0
0
0
0
0
θ6 + 90◦
0
0
0
−90◦
0
−90◦
0
0
θ14 + 90◦0000
θ18 + 90◦
0
0

a
l2
0
0
l3
l3
l2
0
0
l3
l4
0
l1
0
l5
l6
0
0
l7
l8
l6
0
0
l7
l8

d
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

set of quadruple of parameters as
Ti = Tθ j Td j Ta j Tα j . . . . . . . . . . . . (18)where Tθ j , Td j , Ta j , Tα j , ( j ∈ m) represent the rotation matrix of the four steps listed above. The combined matrix
Ti is
⎡
⎤
cos θ − cos α sin θ
sin α sin θa cos θcos α cos θ − sin α cos θ a sin θ ⎥⎢ sin θ⎣ 0
⎦ (19)
sin α
cos α
d
0
0
0
1
We distribute all of the joints according to the parameters in Table 1. In this table, joint 1–4, 5–8, 9–12, 13–16
represent the DH parameters for the rotation angles of theleft arm, right arm, left leg, and right leg, respectively.
The indices of the joints and links are shown in Fig. 3.
By applying the DH parameters shown in Table 1, it iseasy to build up the simulated human skeleton. Based on
the DH parameters, the target for determining a human’s
posture is optimized by rotational angles of each joints.

Fig. 3. Illustration of GNG node weights.

of particles, where each particle represents a bird in theflock. In this paper, we assume that each particle represents a skeleton candidate with a different series of rotation angles. The purpose is to iterate all of the particles
with their velocities of rotational angle, and selecting thebest series with the minimum value, which correspond tothat the best skeleton for representing the human posture.
The speed of the rotational angle in the PSO algorithm
can be represented as
vki = vk−1
+ w1 r1 (Pbesti − Xi ) + w2 r2 (Gbesti − Xi )i
. . . . . . . . . . . . . . . . . . (20)where vki is a vector representing the angle velocities of
the i-th agent at k-th iteration. vki is controlled by threefactors: the global best velocities Gbesti , the personal best
velocities Pbesti and the previous velocities vk−1
. Thus,ithe current angles for xki will be calculated as

θik = θik−1 + vki. . . . . . . . . . . . (21)Different human postures are generated by the angles
calculated from the above equation, depending on the previous knowledge and random factors.
Because the GNG nodes have roughly described structures of the human skeleton, it is necessary to optimize allof the parameters of the skeleton model to optimize therotational angles. In order to calculate the value of the
global best and the personal best, it is necessary to propose a proper evaluation function. Here we optimize thebest skeleton by searching for the minimum value of thefollowing evaluating function:M

3.4. PSO for Human Posture Recognition
We apply PSO to optimize all of the rotational angles
to properly simulate the human posture. The PSO is acomputational method that optimizes a problem by iteratively trying to improve a candidate solution according togiven measures of quality. It was originally proposed by
Kennedy and Eberhard [22], and has been extended to aseries of advanced solutions.
The motivation from the PSO algorithm is inspired by
the flocking behavior of birds nature. It contains a set
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F(x) = ∑ wgi d(gi ) . . . . . . . . . . . (22)i=1where M is the number of GNG nodes that were obtained
from the previous step, wgm is the weight of the m-th GNG
node gm , and the function d(gm ) is represented as⎧− jn 2
g − j 2⎨ − gi2a− i n
2e
if argmin e 2a2 ≤ Kd(gi ) =jn ∈J⎩τotherwise. . . . . . . . . . . . . . . . . . (23)Journal of Advanced Computational Intelligenceand Intelligent Informatics

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where a is a constant, and τ is a constant threshold with alarge value. This evaluation function means that the skeleton with the smallest sum of distances for the total GNG
nodes is more likely to be the fittest posture. Consideringthat each link is represented by a cylinder with a radialthreshold of K, the value of d(i) is valuable if and only
if the node i is inside this cylindrical space. Nodes thatare closer to the axis of the cylinder would have a higher
possibility to be part of it.
Despite the advantages of GNG , it is obvious that GNG
nodes are generated randomly, which means that each of
the node has the same importance for constructing thewhole GNG network. However, they should be separated
into different levels based on their representations of human body. The reason is that different locations in humanbody have different importance when representing the human body. For example, the nodes that surround the elbow provide more information than the nodes located in
the torso, therefore, these nodes should be given a higher
weight when reconstructing the human body.
In this paper, we tried a simple but efficient weighted
methods. It is obvious that nodes that are near to the center would have more edges than those located in the edge.
Therefore we suppose that the weight of the i-th node isrepresented as wi , then the weight is

Fig. 4. Profile of the Xtion sensor.

Table 2. Features of the Xtion sensor.
WeightInterface
Available view angle
fps
Resolution

490g
USB2.070 degree
30 to 60
640*480

wgi = ∑ D(gi , g j ) . . . . . . . . . . . (24)g j ∈G

where D(si , s j ) denotes
D(gi , g j ) =01

if cgi ,g j = 0
otherwise. . . . . (25)This also means that the weight of gi depends on thenumber of edges linked to it, as shown in Fig. 3. It is obvious from the figure that the n1 that contains less edges
than n2 provides more significant information for generating the human skeleton.

4. Experiment Setup and Results
For the experiment in this study, we applied ASUS
Xtion PRO live depth camera as the frame capturing sensor, shown in Fig. 4 and Table 2. Compared with the otherwidely used depth cameras, such as Microsoft Kinect,
Xtion, it has a low-budget projects or systems.
In the first step, we applied the frame differential algorithm to extract the foreground points, which is regarded
as the construction part of human body. Considering thatit contains a large amount of noise, we also applied the
median filter to reduce them. The result can be seen in
Fig. 5.
Figure 6 shows the experiment result comparison between the standard GNG and the GNG-U2. It is obviousthat the GNG-U2 provides a more stable network from aseries of video frames compared with the standard GNG,
the number of points have been reduced dramatically but
the rough structure of the network remained the same.
Next, Fig. 7 shows that the number of points for pro524

Fig. 5. Performance of foreground extraction.

cessing is reduced dramatically after GNG learning. The
left figure shows the point number of the original frame,
whereas the right one shows the number of point for the
GNG network. It is obvious that the number of points has
been dramatically reduced after the GNG learning.
As mentioned earlier, our solution contains a large
number of parameters that need to be optimized, and it isdifficult to localize all of them in one step. To overcome
this issue, we seek to optimize part of the parameters in
the initial step, and make the prediction in the following
steps.
To locate the human body as fast as possible, we madethe restriction at the initial part, i.e., we defined a special
initial posture. In this experiment, we suppose that thehuman skeleton as a special T-posture because it is easier to determine the length of each link of such posture.
In this case, all of the parameters of the rotational angles

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Fig. 6. Experiment result between the standard GNG and the GNG-U2 in a series of input frames. The first row represents theoriginal frame, the middle row shows the standard GNG, and the last row shows the GNG-U2.

Fig. 7. Comparison of point number for processing for the same given input frames.

are fixed at the initial part, and the optimized parameters
are only the spatial coordinates of the skeleton. Once theoriginal coordinates are located, the following predictions
will be simpler.
The result of the human posture is shows in Fig. 8,
where the top row shows the original structure and thebottom row shows the result of human posture recognition.
The computational time in PSO is affected by the number of particles and iterations. We evaluate the run time
cost based on three different conditions with different iteration times. Fig. 9 illustrates the time taken by different
particles and iterations. It is obvious that the time rises
dramatically if the iterations increase. It is important tochoose a proper iteration under different environment.

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5. Discussion and Conclusion
In this paper, we proposed an unsupervised human posture recognition method that is different from most of theprevious proposed methods. The proposed method contains a series of unsupervised learning algorithms, suchas GNG, and PSO. Thus, no pre-training data is required,
which is crucial for real world applications. By applying
GNG, it deduces the run time cost dramatically comparedwith tackling the whole point cloud directly. In addition,
the PSO made it possible to find the best simulated posture without any training.
Overall, this paper provides a preliminary method forhuman posture recognition. However, the run time costfor optimization would be a factor that limits the method
for real-time implementation. In the next stage, we will
focus on reducing the run time cost of optimization step.

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Fig. 8. Experiment results of human posture recognition generated by the proposed method.

Fig. 9. Experiment result for different iteration times.

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Journal of Advanced Computational Intelligenceand Intelligent Informatics

Vol.23 No.3, 2019

Human Posture Recognition for Estimation of Human Body Condition

Name:

Name:

Wei QuanYuichiro Toda

Affiliation:

Affiliation:

Graduate School of Systems Design, Tokyo
Metropolitan University

Assistant Professor, Graduate School of Natural
Science and Technology, Okayama University

Address:

Address:

6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

3-1-1 Tsushima-Naka, Kita, Okayama, Okayama 700-8530, Japan

Brief Biographical History:

Brief Biographical History:

2013 Received Master degree from Graduate School of Information,
Production and Systems, Waseda University

2017- Visiting Assistant Professor, Department of Systems Design, Tokyo
Metropolitan University
2018- Assistant Professor, Graduate School of Natural Science andTechnology, Okayama University

Main Works:• W. Quan and N. Kubota, “Evolutionary people tracking for robot partnerof information service in public areas,” Int. Conf. on Intelligent Robotics
and Applications, 2017.

Main Works:

Name:• A. A. Saputra, Y. Toda, J. Botzheim, and N. Kubota,“Neuro-Activity-Based Dynamic Path Planner for 3-D Rough Terrain,”IEEE Trans. on Cognitive and Developmental Systems, Vol.10, No.2,
pp. 138-150, 2018.
• Y. Toda and N. Kubota, “Evolution Strategy Sampling Consensus for
Homography Estimation,” J. Adv. Comput. Intell. Intell. Inform., Vol.20,
No.5, pp. 788-802, 2016.

Jinseok Woo

Membership in Academic Societies:• The Institute of Electrical and Electronic Engineers (IEEE)• The Japan Society of Mechanical Engineers (JSME)

Affiliation:
Research Assistant Professor, Graduate Schoolof Systems Design, Tokyo Metropolitan University

Name:
Naoyuki Kubota

Address:
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

Affiliation:

Brief Biographical History:

Professor, Graduate School of Systems Design,
Tokyo Metropolitan University

2011 Received Master of Engineering degree from Graduate School of
Systems Design, Tokyo Metropolitan University
2017 Received Ph.D. degree from Graduate School of Systems Design,
Tokyo Metropolitan University
2017- Research Assistant Professor at Intelligent Mechanical Systems,
Graduate School of Systems Design, Tokyo Metropolitan University

Address:

Main Works:• J. Woo, J. Botzheim, and N. Kubota, “A Socially Interactive in Japanese
Robot Partner Using Content-based Conversation System for Information
Support,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.6,
pp. 989-997, 2018.
• J. Woo, J. Botzheim, and N. Kubota, “System Integration for Cognitive
Model of a Robot Partner,” Intelligent Automation & Soft Computing(AUTOSOFT), pp. 1-14, 2017.

Membership in Academic Societies:• The Institute of Electrical and Electronic Engineers (IEEE)• The Japanese Association of Research on Care and Welfare (JARCW)

6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

Brief Biographical History:
2005- Associate Professor, Department of Systems Design, Tokyo
Metropolitan University
2007 Visiting Professor, University of Portsmouth
2009-2012 Visiting Professor, Seoul National University
2012- Professor, Graduate School of Systems Design, Tokyo Metropolitan
University

Main Works:• D. Tang, B. Yusuf, J. Botzheim, N. Kubota, and C. S. Chan, “A novel
multimodal communication framework using robot partner for aging
population,” Expert Systems with Applications, Vol.42, Issue 9,
pp. 4540-4555, 2015
• N. Kubota and Y. Toda, “Multi-modal Communication for
Human-friendly Robot Partners in Informationally Structured Space,”
IEEE Trans. on Systems, Man, and Cybernetics – Part C, Vol.42, No.6,
pp. 1142-1151, 2012.

Membership in Academic Societies:• The Institute of Electrical and Electronic Engineers (IEEE)• Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)• The Society of Instrument and Control Engineers (SICE)

Vol.23 No.3, 2019

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