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Sensors and Materials, Vol. 33, No. 9 (2021) 3153–3168
MYU Tokyo
S & M 2678

Surface Electromyography (sEMG)-based Intention Recognition
and Control Design for Human–Robot Interactionin Uncertain Environment
Junbao Gan,1 Ning Wang,2* and Lei Zuo3
1Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering,South China University of Technology, Guangzhou 510640, China

2Bristol Robotics Laboratory, University of the West of England, Frenchay, Coldharbour Ln, Bristol BS34 8QZ, UK
3School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China(Received December 20, 2020; accepted April 16, 2021)

Keywords: human–robot interaction, surface electromyography, barrier Lyapunov function, radial basis
function neural network

An important direction of human–robot interaction (HRI) is making robots respond tocomplex and dexterous tasks intelligently. To achieve this, biological signals based on surface
electromyography (sEMG) have widely been used to identify human intentions rapidly andeffectively. We propose an algorithm that can recognize human intentions conveyed by different
hand gestures through analyzing sEMG data. This will facilitate the selection of the mostappropriate interaction mode and level during HRI for the robot. We also propose an admittancecontrol framework combining a tan-type barrier Lyapunov function (BLF) and a radial basis
function neural network (RBFNN) to ensure the interaction and tracking performance and toguarantee the stability of the system in uncertain environments. Experiments performed on a
Baxter robot verify the effectiveness of the proposed framework.

1.

Introduction

Nowadays, robots are widely used in industrial automation, rehabilitation therapy, surgery,and daily life,(1–5) and inevitably interact with the external environment in actual application. It
is essential to ensure the intelligent response of robots to complete more complex, dexterous, andmultifunctional tasks in an unstructured environment. Therefore, human–robot interaction(HRI) has become a research hotspot.
Compliant control and tracking control are used for intelligent HRI. A lot of meaningful
work on compliant control has been carried out, and two mainstream methods are hybrid
position/force control and impedance control.(6–9) Without considering the dynamic coupling
between environment and robot, the accuracy of hybrid position/force control cannot be
guaranteed. In contrast, impedance control aims to adjust the mechanical impedance to thetarget impedance, which has better robustness and safety. Impedance control is mainly divided*Corresponding author: e-mail: Katie.Wang@brl.ac.uk

https://doi.org/10.18494/SAM.2021.3230

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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into impedance control and admittance control according to the causality of the controller.
Owing to the inertia and friction of robots, impedance control is usually suitable for dynamic
interaction in a rigid environment. Admittance control can obtain the reference trajectory fromthe required admittance model and measured external force, and is mostly used in flexible
environments.(10,11) Therefore, we apply admittance control to deal with the interaction with aflexible environment. Accurate kinematic information of a robot is usually provided by the
manufacturer, but the dynamics of a robot is uncertain in actual situations, which will have anegative effect on the performance of tracking control. However, the available input and output
data could be used to approximate unknown robot dynamics. Thus, a controller based on a radialbasis function neural network (RBFNN) is widely used.(12–14) Moreover, the position and the
speed may change markedly upon interaction owing to low control accuracy, so the transient
performance should be considered for tracking control. Many studies utilized a barrier Lyapunov
function (BLF) to change the transient performance of the control system.(15–18) Li et al.
developed a robust adaptive fault-tolerant control method based on the BLF to ensure transient
performance and robustness against actuator failure.(15) Considering the internal force between
dual arms, Yang et al. used the BLF to specify the tracking performance in transient and steadystates to achieve effective cooperation between dual arms.(16) Sun et al. combined the timevarying BLF technology and a high-gain feedback method to limit the output tracking error in apredefined arbitrary area.(17)One of the most critical problems in HRI is making robots understand the movement
intention of humans. Common intention recognition techniques include the use of vision, natural
language processing (NLP), and biological signals.(19–24) The visual method can easily produce
incorrect judgments when the view is blocked by an object, whereas for the NLP method,industrial applications are limited by the need for voice acquisition and text input. However,biological-based intention recognition methods usually capture the operator’s movement
intention rapidly and effectively. A surface electromyography (sEMG) signal is one of the mostwidely used biological signals for interaction. Kong et al. proposed a shared control strategy thatused sEMG signals to control the direction and speed of mobile robots.(25) To obtain adaptive
impedance control effectively, Zeng et al. used sEMG to extract the characteristics of humanarm stiffness and then mapped the estimated stiffness to an impedance controller.(26) Yang et al.
proposed a variable gain control mechanism to make a remote operating system naturallyinteract with the external environment by utilizing a task learning framework and the recordedsEMG signal.(27) In all these studies, sEMG signals were used to achieve more intelligent HRI.
However, the force applied to the same object may be different in different situations, which may
cause the falling of objects in HRI.
In this paper, we propose an algorithm to make the robot automatically select the interaction
mode and level during HRI by recognizing human intentions through their hand gestures. A
BLF-based RBFNN control framework is applied to a constraint manipulator, which interacts
with an unknown environment. Compared with existing research, the main contributions of thispaper are as follows.
(1) We propose an algorithm for human intention recognition that can automatically select theinteraction mode and level according to the classification results of hand gestures.

Sensors and Materials, Vol. 33, No. 9 (2021)

3155(2) We present a control framework combining a tan-type BLF and an RBFNN that can limit the
tracking error within a certain range and achieve good transient performance during HRI.
The rest of this paper is organized as follows. In Sect. 2, related preliminary research is
described. In Sect. 3, the method of human intention recognition and the design of the controller
with its proof of stability are introduced. In Sect. 4, the effectiveness of our proposed method isverified through experiments conducted on a Baxter robot. Section 5 shows conclusions.

2.

Preliminaries

2.1

Robot dynamic description

Murray et al.(28) have derived the formulas for robot dynamics in detail, and the following
formulas and properties are based on their work. The dynamics of a robot in the joint space is
M q (q )q + Cq (q, q )q + Gq (q ) =u+ f ,(1)

where q, q, and q are the coordinate, speed, and acceleration in the joint space, respectively.
M q (q ) ∈  n×n is the inertia matrix of the manipulator, Cq (q, q ) ∈  n×n represents the centrifugal
and Coriolis force vector, Gq (q ) ∈  n×1 represents the gravity torques, u ∈  n is the vector of thecontrol input, and f ∈  n is the vector of the measured interaction force, which is the force
exerted by the human on the robotic arm.
Since the manipulator is controlled in Cartesian space, the above formula is transformed
from the joint space to Cartesian space as

M x (q ) 
x + C x (q, q ) x + G x (q ) =+τu τ f ,(2)

 are the coordinate, speed, and acceleration in Cartesian space, respectively.
where x, x , and x
The remaining variables are transformed from the joint space to Cartesian space, and the robot
dynamics have two properties as follows.

M x = J −T M q J −1

(

(3)

)

=
C x J −T Cq − M q J −1J J −1

(4)

G x = J −T Gq(5)τ u = J −T uu(6)τ f = J −T f f

(7)

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Property 1: M x (q ) is a symmetric and positive definite matrix.
 (q ) is a skew-symmetric matrix.
Property 2: 2C x (q, q ) − M
x
2.2

RBF neural network

An RBFNN is widely used to deal with uncertainties in various systems because of its
universal approximation of(29)ϕ ( Z ) = W *T S ( Z ) ,(8)

where ϕ ( Z ) ∈  n×1, W *T ∈  n×k denotes the ideal weight matrix, which cannot be obtained inpractice, Z ∈  m×1 is the input vector, and S ( Z ) ∈  k×1 is a vector composed of radial basis
functions. In general, we select the Gaussian function as the radial basis function, and the ith
S(Z) is calculated as(

Z − ui
Si =−
exp

2

)

/ Ωi2 ,

(9)

where ui ∈  m×1 is the central vector and Ωi is the width of the radial basis function. The
dynamic parameters of the manipulator are*T
M x = WMSM ,C x = WC*T SC ,(10)

G x = WG*T SG .
ˆ , Cˆ , and Gˆ are the dynamic parameters of the manipulator approximated using theMxxx
RBFNN in Eq. (8),Tˆ=M
x WM S M + ε M ,=CˆWˆ T S + ε ,
x

C

C

C

(11)

=
Gˆ x Wˆ G T SG + ε G ,where ε M , ε C , and ε G represent the bounded approximation errors of the neural network. We
define the deviation signal of the RBFNN is equal to estimated value minus actual value, which
would not affect the stability analysis.

 T
=
M
x WM S M + ε M=
CW T S + ε
x

C

C

C

=
G x W G T SG + ε G(12)

Sensors and Materials, Vol. 33, No. 9 (2021)

3.

Human Intention Recognition and Controller Design

3.1

Human intention recognition based on sEMG signal

3157

sEMG is an important feature for the non-invasive recognition of muscle activity. Different
intensities of an sEMG signal can represent different muscle movements. To select sEMG
features initially before preprocessing, we obtain eight channels of hand gestures and use a
320-ms-long sliding window to intercept 64 frames of data for each channel as the feature of one
sampling. Human intention recognition is based on the following structure including an offline
training process and an online intention estimate, as shown in Fig. 1. The training process isoffline, and the features of the raw sEMG signals of different hand gestures are extracted
through a low-pass filter and a sliding window. In practice, the feature of the raw sEMG signal is
extracted rapidly and then imported as an input to a convolutional neural network (CNN)classifier for hand gesture recognition online.
We set up six hand gestures to represent three interaction modes and three interaction levels
as shown in Fig. 2. Among them, gestures 1 to 3 represent the pause, forward, and backwardmodes, respectively. Gestures 4 to 6 represent levels 1, 2, and 3, respectively. In the pause mode,
the robot stops its motion to ensure safety during HRI until the next hand gesture is recognized.
In the forward mode, the human pushes the robot to move in the forward direction, while in the
backward mode, we add a virtual force to the manipulator to keep the robot following the humanFig. 1.

(Color online) Training offline and estimate intention online of sEMG signal.

Fig. 2. (Color online) Hand gestures associated with three interaction modes (gestures 1–3) and three interactionlevels (gestures 4–6).

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in the backward direction. These two modes represent common back-and-forth motion during
HRI. However, when the human and robot hold an object collectively in the backward mode, theobject is more likely to fall if the robot cannot follow the human motion with the same pace. To
solve this problem, we set different interaction levels. A higher interaction level means a larger
force will be exerted on the object by the robot. In this way, the interaction levels, i.e., the forces
exerted by the robot, are determined according to the manipulation task, e.g., the weight of theball in this example. Although this way of setting interaction levels applies to both the forward
and backward modes, the levels for these two modes are independently determined.
We propose an algorithm that can automatically switch the interaction mode and level byhuman intention recognition. Algorithm 1 shows the pseudocode for achieving this process. In
Algorithm 1, we define two flags: flag1 represents the interaction mode and flag2 represents theinteraction level. We set three levels for flag2, so that it can cope with three objects with different
weights. These levels, which represent the interaction force, can easily be changed according to
the needs in other HRI applications. The threshold setting is related to the additional sEMG
signal activation value when we change the hand gesture, and it is set to 30% of the maximumsEMG activation value of the minimum-intensity exercise. As also shown in Algorithm 1, when
an sEMG signal reaches the set threshold, it is fed to a CNN classifier to produce theclassification result. When the first gesture is identified successfully, the robot will moveaccordingly, e.g., when gesture 1 is identified, the robot will stop and enter the pause mode toensure safety. Otherwise, if the classification result is either gesture 2 or 3, flag1 will be changed
to choose either the forward or backward mode. After setting flag1, we need to define flag2.
Now we use gestures 4, 5, and 6 to define flag2. When the second hand gesture is identified, wechoose the interaction level accordingly by changing flag2. The robot will then switch to theappropriate interaction mode and level to facilitate the physical interaction with the humanpartner.
Algorithm 1
Framework of automatically switching interaction mode and level.
Input: Different sEMG signals
Output: the interaction mode flag1 and level flag2 which control robot motion
1: flag1 = 0, flag2 = 0
2: while not receive end signal
3: accept and update sEMG signal
4: if ∑ sEMG 2 ≥ Threshold5:
input to CNN and obtain sEMG signal classification result κ
6: if κ ∈{gesture 1}
7:
mode = pause mode
8:
continue
9: else if κ ∈{gesture 2, gesture 3}
10:
flag1 = corresponding mode of κ , mode∈{forward mode, backward mode}
11:
continue
12: else if flag1≠ 0 and κ ∈{gesture 4, gesture 5, gesture 6}
13:
flag2 = the corresponding level of κ, level∈{level 1, level 2, level 3}
14:
send flag1 and flag2 to robot
15: else
16: goto step 3

Sensors and Materials, Vol. 33, No. 9 (2021)

3.2

3159

Design of RBFNN controller based on BIF

We design the RBFNN controller based on the BLF and introduce the design process andstability analysis in detail in this section. Figure 3 shows the general framework of our proposed
controller. The Cartesian tracking error is defined as z1= x − xd ,
 z2= x − α ,

(13)

T

where z1 = [ z11  z1n ] , xd = [ xd 1  xdn ] is the desired position in Cartesian space, andα is the virtual controller, which can be designed asT=
α x d − A ,(14) πz112  kzcos 11 11
2 
 2a1  
where A = , k1i > 0, i = 1, ..., n.
 z 2
 k1i z1i cos  π 1i   2a 2   i 
Let x1 = x and x2 = x , then Eq. (2) can be transformed into x1 = x2 ,−1=
 x 2 M x τ u + τ f − C x x − G x .

(Fig. 3.

)(Color online) Structure of RBFNN controller.

(15)

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The robot interacts with the unknown environment through admittance control, and theadaptive control torque τ u isnk a2

 πz 2 n πz 2 τu =− k p z2 − ( z2T ) + ∑ 1i i tan  1i2  − ∑z1i sec  1i2  2a 
 2a 
π1
 i  1
 i 

(16)

ˆ α + Cˆ α + G ,− kr sgn( z2 ) − τ f + Mxxx
where kp > 0, ( z2T ) + is the pseudoinverse of error z2, sgn(*) is the sign function, and kr > 0 withkr > ε M α + ε C α + ε G .
3.3

Stability analysis

In this part, we utilize the Lyapunov method to strictly evaluate the stability of the control
system and derive the stable adaptive update law of the RBFNN in detail. Owing to the low
control accuracy, the position and speed may change markedly during an interaction, resulting ina strong collision and unpredictable results. Therefore, we introduce a BLF(30) to solve thisproblem:V (t ) = πz 2 a2tan  2  , 2a 
π(17)

where z is the system state restricted by z < a.
Consider a continuous Lyapunov function V(t) > 0, where ∀t ∈ R +, V(0) is bounded. It can beconcluded that if the derivative of the Lyapunov function satisfies the following inequality, thenV(t) is bounded.(31)V (t ) ≤ − ρV (t ) + σ(18)

Consider the Lyapunov function

 πz1i 2  1 T
ai2
1
1
1
tan
∑ π  2a 2  + 2 z2 M x z2 + 2 W M TW M + 2 W cTW c + 2 W GTW G ,
1
 i 

(19)

 πz1i 2  1 T


T 

s
ec
z
z
∑ 1i 1i  2a 2  + 2 z2 M x z2 + z2T M x z2 + W M TWˆ M +W C Wˆ C +W GT Wˆ G.
1
 1i 

(20)n=
V (t )=V (t )n

 (q ) is a skew-symmetric matrix, upon substituting Eqs. (13)–(16)Because 2C x (q, q ) − Mxinto Eq. (20), we obtain

Sensors and Materials, Vol. 33, No. 9 (2021)

3161

n
n
 πz 2 k a2
 α + C α + G−∑k1i z1i 2 − z2T k p z2 − ∑ 1i i tan  1i2  + z2T M
V (t ) =xxx
 2a 
π
1
1
 i 



+ z2T ( ε M α + ε C α + ε G − kr sgn( z2 ) ) + W M T Wˆ M + W C T WC + W G T WG .

(

)

(21)

The stable adaptive update law of the RBFNN isWˆ M =− S M α z2 − β M Wˆ M ,(22)

Wˆ C =− SC α z2 − βCWˆ C ,(23)

Wˆ G =− SG z2 − βGWˆ G ,(24)

where βM, βC, and βG represent the robust items of the RBFNN. Substituting Eqs. (22)–(24) into
Eq. (21) givesnn πz 2 k a2V (t ) =−∑k1i z1i 2 − z2T k p z2 − ∑ 1i i tan  1i2  − β M W M T Wˆ M − βCW C T Wˆ C − βGW G T Wˆ G
 2a π11
 i −W T (W + W * )Ta k i n g t h e e q u a l i t y −W T W * =1−W T W * ≤ (W T W + W *T W * ) into account, we obtain2

andYo u n g ’s

inequalit yn πz 2 k a2V (t ) ≤ − z2T k p z2 − ∑ 1i i tan  1i2  − β M W M T Wˆ M − βCW C T Wˆ C − βGW G TWˆ G
 2a 
π1
 i n
 πz 2 k a2
1≤ −∑ 1i i tan  1i2  − z2T k p z2 − W M T W M + W C T W C + W G T W G


2
π
1
 2ai 1
+ W M *T W M * + WC *T WC * + WG *T WG * .
2

(

(

(25)

)

(26)

)

Equations (18) and (26) have the same form, from which we obtain()

1
W M *T W M * + WC *T WC * + WG *T WG * ,2 2λ ( k )  
ρ = min  2k11 , 2k1n ,  min p   .
 λmax M x  


=
σ

(27)

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Sensors and Materials, Vol. 33, No. 9 (2021)

Experiments

Experiments are carried out with the Baxter robot manipulator, as shown in Fig. 4. The
human partner interacts with the robot with his left arm. The robot recognizes his intention byidentifying the gesture posed by his right hand. There is a ball held between the robot end
effector and the left hand of the human partner. The end of the Baxter robot arm is equippedwith a Mini45 force sensor to measure the contact force in real time. The human partner
continuously adjusts the force he applies to the contact force exerted by the robot to keep the ballin place without falling off. Raw sEMG signals are collected by a Myo armband worn by thehuman partner on his right arm. In the first experiment, we verified that the proposed control
framework could guarantee transient performance during interaction. In the second experiment,
we verified that the proposed method could make the robot change its interaction mode and levelautomatically according to human intentions conveyed by hand gestures.
4.1

Test of RBFNN controller based on BLF

In the first experiment, we use a held balloon to interact with the left arm of the Baxter robot
while ensuring transient performance. The initial Cartesian position is [x, y, z] = [0.60, −0.33, 0.50] m
and the final Cartesian position is [x, y, z] = [0.60, 0.30, 0.50] m. We push the balloon along the
Y-axis to make the robot’s left arm move from the initial position to the final position andobserve the variation of the force, position, and position error. Transient and steady performances
are guaranteed by utilizing the BLF-based RBFNN constraint controller designed using Eq. (16).
The parameter is ai = 0.04 and the control gains are k1i = 80, k p = 60, and k r = 5. Furthermore, to
use the RBFNN to estimate the uncertain parameters of the robot dynamics, n = 37 neuralˆ (q ) and Gˆ (q ) and 2n = 2 × 3 are employed for Cˆ (q ). The
network nodes are employed for Mxxx

Fig. 4. (Color online) Process of HRI (left arm interacts with robot while right arm’s hand gestures are used forhuman intention recognition).

Sensors and Materials, Vol. 33, No. 9 (2021)

3163

1 2 n×1ˆ (00)∈=ˆ (MG(00)∈=(00)∈==
0n∈×102∈n×, Cnˆ×x1(,0Cˆ)x=,G
weight matrices are initialized as M
and
G)xx=
(0)
0 n∈×1 n×1, andx 0)x=x (,0
the parameters of the RBFNN update law are chosen as βM = βC = βG = 5.
The force on the Baxter robot’s left arm during the interaction is shown in Fig. 5. The initialreceived force is not 0. This is because the Mini45 force sensor has errors due to gravity. The
force received by the robot remains at about 3 N with tiny fluctuations. Because of ergonomic
limitations, a human cannot exert an absolute constant force during interaction. The
experimental results show that the admittance control is effective in force control for interaction
with a flexible object.
The position and tracking error in the Y-axis are shown in Fig. 6. It can be seen from Fig. 6that except for the larger tracking error at the beginning and end, the errors at other times are allapproximately zero. The actual trajectory has good tracking performance for the reference
trajectory, and the tracking error is kept within 4 cm, which is a small range. The experimental
results show that our proposed BLF-based RBFNN controller limits the tracking error to a value
within a certain range and ensures a transient response.

Fig. 5.

(a)Fig. 6.
error.

(Color online) Force measured during HRI.

(b)(Color online) Y-axis position and tracking error during HRI. (a) Y-axis position and (b) Y-axis tracking

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4.2

Sensors and Materials, Vol. 33, No. 9 (2021)

Human intention recognition based on sEMG signal

In the second experiment, we change the hand gestures to make the robot perform different
interaction modes and levels to achieve human intention recognition. The raw sEMG signals
corresponding to different gestures of a volunteer are shown in Fig. 7. It is observed that different
features are associated with the hand gestures. The gestures are dynamic since static gestures
require upper limb muscles to stay taut all the time, which will inevitably produce muscle fatigue
and lower the success rate of gesture recognition. The recognition learning curves of different
gestures are shown in Fig. 8. As the number of iterations increases, the loss decreases and theaccuracy increases gradually, indicating that the features of different hand gestures could beacquired after passing through a CNN classifier to achieve good classification performance.
The robot could change its interaction mode and level according to human intentions, as
described in detail in Sect. 3.1. According to the classification result of the hand gestures, wefirst set flag1 to choose the interaction mode. Then, by recognizing the next hand gesture, wechanged the value of flag2 to define the interaction level. Figure 9 shows the variation of theinteraction force for three sample conditions. Specifically, Fig. 9(a) shows the force switching toa different level in the same mode. The red line represents the force in the forward mode and inlevel 1, and the green line represents the force in the forward mode and in level 2, whichindicates an increased force level. Figure 9(b) shows the force switching to a different mode in
the same level. The red line represents the force in the forward mode and in level 1, and the

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 7. (Color online) Raw sEMG signals corresponding to different gestures: (a) gesture 1, (b) gesture 2, (c)gesture 3, (d) gesture 4, (e) gesture 5, and (f) gesture 6.

Sensors and Materials, Vol. 33, No. 9 (2021)

Fig. 8.

(a)3165(Color online) Recognition learning curves of different gestures.

(b)

(c)

Fig. 9. (Color online) Force measurement in different interaction modes and levels. (a) Switching to different levelin same mode. (b) Switching to different mode in same level. (c) Switching to different mode and level.

green line represents the force in the backward mode and in level 1. Because a virtual force isadded according to the different level in the backward mode with level 1 for both interactionlevels, the absolute values of the forces are different in Fig. 9(b) for the forward and backwardmodes. Moreover, although the forces are both in the negative direction, the direction of motion
is opposite for these two modes. Owing to the accuracy of the sensor, the forces measured in theforward mode and in level 1 in Figs. 9(a) and 9(b) are not exactly the same. However, thevariation range is within 0.5 N. Figure 9(c) shows the force switching to a different mode and adifferent level. The red line represents the force in the forward mode and in level 1, and the green
line represents the force in the backward mode and in level 3.
The above experimental results prove that our proposed method can effectively identify
human intentions and flexibly achieve intelligent HRI. Humans can change the interaction modeand level according to different situations when interacting with a robot while the contact force
fluctuates around a certain value.

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Conclusions

In this paper, we propose an algorithm of human intention recognition through identifying
various hand gestures, which makes the robot automatically change the interaction scheme toadapt to different HRI tasks. An admittance control framework combining a tan-type BLF andan RBFNN has also been proposed to achieve desirable interaction and tracking performance
while guaranteeing stability. HRI experiments with a Baxter robot show that, first, the proposed
control framework gives the robot good tracking and transient performances. Second, the humanintention recognition algorithm allows the robot to adapt its interaction mode and level according
to human hand gestures in real time, which ensures the exertion of the most appropriate contact
forces by the robot, greatly improving the success rate of the HRI task. This framework will be
extended to accommodate more challenging scenarios and tasks in the future by exploring therelationship between the activity level of human upper limb muscles and the robot position in an
HRI process to achieve continuous intention recognition, and by realizing optimal interaction
with flexible objects.

Acknowledgments
This work was supported in part by the National Nature Science Foundation of China (NSFC)
under Grant 61803039.

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About the Authors
Junbao Gan received his B.S. degree in automation from South China
University of Technology, Guangzhou, China, in 2018, where he is currently
pursuing his M.S. degree. His current research interests include human–robot
interaction, human intention recognition, and adaptive control.

Ning Wang is a senior lecturer in robotics at the Bristol Robotics Laboratory,
University of the West of England, United Kingdom. She received her M.Phil.
and Ph.D. degrees in electronics engineering from the Department ofElectronics Engineering, The Chinese University of Hong Kong, Hong Kong,in 2007 and 2011, respectively. Ning has rich project experience: she has beena key member of the EU FP7 Project ROBOT-ERA, the EU Regional
Development Funded Project ASTUTE 2020, and industrial projects with UKcompanies. She has received several awards including Best Paper Award of
ICIRA’15, Best Student Paper Award nomination of ISCSLP’10, and Award of3168

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Merit of the 2008 IEEE Signal Processing Postgraduate Forum. Her researchinterests lie in signal processing, intelligent data analysis, human–robot
interaction, and autonomous driving.
Lei Zuo (M’17) received his B.Sc. and Ph.D. degrees from Northwestern
Polytechnical University, Xi’an, China, in 2011 and 2017, respectively. Since
2017, he has been with the School of Electronic and Control Engineering,
Chang’an University, Xi’an, where he is currently an assistant professor. His
current research interests include multiagent systems, coverage control, model
predictive control, and vehicle platoon control.