6 Expert Systems - Raj Aug 2021.pdf

O R I G I N A L A R T I C L E
Human muscle rigidity identification by human-robot
approximation characteristics framework on internet of things
platform
Rajalakshmi Selvaraj1
| Venu Madhav Kuthadi1
| S. Baskar2
1
Department of CS & IS, Botswana
International University of Science and
Technology, Palapye, Botswana
2
Department of Electronics and
Communication, Karpagam Academy of Higher
Education, Coimbatore, India
Correspondence
Venu Madhav Kuthadi, Department of CS & IS,
Botswana International University of Science
and Technology, Palapye, Botswana.
Email: venumadhav.kuthadi@yahoo.com
Abstract
In the health care system and Internet of Things (IoT) platform, medical care robotics is
becoming one of the quickest expanding areas of robot technology. The integration of
robotics and human knowledge identifies human muscle rigidity from the healthcare
data obtained from the wearable sensor. In an IoT platform, Electromyography is a
method used for evaluating and tracking the electrical activity of muscles. The transfer-
ring of human muscle rigidity to a robot facilitates the robot to obtain resistive manage-
ment initiatives in a useful and effective way while carrying out physical interaction
activities in unstructured surroundings. The major challenges to overcome the
unpredictability during physical interaction allow a robot to realize the individual behav-
iour with adaptability and versatility of muscles. Therefore, in this article, Human-Robot
Approximation Characteristics Framework (HRACF) has been proposed for developing
physiological communication between humans and robots. HRACF permits robots to
understand differential resistive abilities of muscles from human presentations. The
pulses collected from Electromyography are used to retrieve human arm muscle rigidity
during activity presentation. The characteristics of motion and rigidity are concurrently
modelled using an estimation and approximation model with a logistic regression
obtained by IoT devices. The analysed human arm muscle rigidity is then connected to
the robot impedance regulator. HR model uses an optimized resistive approximator to
measure the creative variables of the robot and continue driving to monitor the quoted
pathways at the time of interaction. The relationship between motion data and rigidity
data is systematically coded in the HR model. HRACF makes it possible to detect uncer-
tainties through space and time that facilitates the robot to meet rigidity specification
to 98[Nm/Rad] and error rate to 0.15% during physical interaction.
K E Y W O R D S
electromyography, human muscle rigidity, human-robot communication, internet of things,
motion, physical interaction, wearable sensors
1 | INTRODUCTION TO HUMAN-ROBOT INTERACTION IN THE HEALTHCARE INDUSTRY
ON IOT PLATFORM
The rapid aging community is becoming a worldwide issue in recent centuries, affecting various social areas, particularly the health care sector
(Bloom & Cadarette, 2019). The healthcare system has shifted from an accessible, tiny, singular belt to a sealed, large, multiple belts in the IoT
Received: 10 June 2021 Revised: 22 July 2021 Accepted: 24 August 2021
DOI: 10.1111/exsy.12824
Expert Systems. 2022;39:e12824. wileyonlinelibrary.com/journal/exsy © 2021 John Wiley & Sons Ltd. 1 of 15
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sector (Al-Hanawi et al., 2019). Home healthcare robotics in specific IoT platforms has been recognized as an important healthcare system
approach (Kowalczuk et al., 2020). Interaction between humans and robotic engineering is a field of research that understands, develops, and
evaluates robotic systems for or with people. The interaction of robots with humans, by definition, calls for communication. Human beings and
robots interact as peers or companions in social interactions. It is noteworthy that people and robots are equivalent: People believe, and sched-
ules and robots effectively implement duties (Choi et al., 2020). Several other studies have recently shown that the communication between
human-robot and human-like movements is an essential factor for healthcare professionals in the IoT sector (Agovino et al., 2021). These
robots can help employees pick items or transport goods across the warehouse, referred to as collaborative robots or collaborative robots. The
employees can focus more on value-generating tasks by assigning menial, demanding tasks to robotics. Such features may affect the usability
and functionality of the system, particularly when old people or people with disabilities are treated (Gao et al., 2020). Many other additional
robots are communicative and uncomfortable, with which the controller requires to be trained to control the machine in detail (Lember
et al., 2019). Robots can help people and enhance productivity significantly, but still, there is sometimes good communication in attempting to
cooperate on physical activities (Crawford et al., 2020). Medical robots help relieve physicians from routine tasks by taking time off more
stressful responsibilities and safer and less expensive medical procedures. It could carriage dangerous substances in small places and performs
precise surgery.
Display of various programming is one of the greatest effective options in which robots can effectively achieve activities through the tran-
sition of inventive human handling skills in healthcare (Talal et al., 2019). The best way to enable robots with a high standard of interactive abil-
ity is to discover the fundamental standards of good sensor actuators and incorporate multidimensional data into robots that influence
healthcare regulations IoT sector (Rostamzadeh et al., 2020). Robots may be used in the room or the hall rotating on the spot. People can turn
and tilt their heads and move your arms, wrists, and fingers, whether to lift and carry things, touch people, or gesture. The neurological study
has demonstrated that people with a consolidated immune system can implicitly adjust limb impedance to interact in different circumstances
(Grewal et al., 2020).
Many studies in healthcare are motivated by living person neurology and cell biology that have managed to switch surface electromyography
of human muscle rigidity regulatory competencies into robots and have produced promising outcomes in the IoT domain (Raldi et al., 2019). The
surface electromyography indicators gathered by the human muscles demonstrate the standard of authentication of skeletal muscles (Moreaux
et al., 2020). The data collected from electromyography can predict and remove the strength of the human brain joint or the end receptors on a
real-time basis throughout the implementation of the activity (Ha et al., 2019).
Robots are never adequate for patients; however, they can lead to less stress for doctors and physicians. This is because the surgery is not
that long or tedious without the assistance of a robot; the surgeons spend less time under operation and stay in uncomfortable positions. One
of the greatest advantages of the bio-inspired human to robotic conductivity transmission is recognizing the responsive impedance authority
for robotic weapons, which shows improved results by a series of works than stability analysis in IoT (Bardati et al., 2020; Hentout
et al., 2019). The varying conductivity features are acquired through a time-absorbing method that can limit the structure's capability for practi-
cal systems (Bardati et al., 2020; Tun et al., 2020). In the job area, the differential rigidity pattern is partly calculated based on the quantified
strength utilizing an existing high precision force detector on the robot terminal, thus raising the Human-Robot Interactions overall costs in
healthcare application and IoT platform (Al-Khafajiy et al., 2019; Massari et al., 2019). Based on the above discussion, the frame developed
allows robots to evaluate the resistive capacity of muscle, and the electromyography signal extracts the rigidity of arm muscle during work. At
the time of interaction, an optimized resistive approximate evaluates robot parameters and monitors the route. In the HR model, the relation-
ship between motion and rigidity is coded. The major disadvantage with the transmission of conductivity abilities is the rigidity description
throughout the implementation of robotic tasks (Barnes et al., 2020). It is frequently inadequate for the robot to replicate the studied policy
measures of people, particularly when handling activity circumstances, unlike the presentation (Fischer et al., 2020). To overcome the issues of
motion and rigidity, HRACF has been proposed. In the HRACF, the interaction is developed among Robots and humans. The main contribution
of HRACF is described as follows
• The developed framework allows robots to estimate the resistive abilities of muscle, and the signal gathered from electromyography extract
the arm muscle rigidity during work demonstration.
• Optimized resistive approximator evaluates the parameters of robots and tracks the route at the time of interaction. The connection between
the motion and the rigidity is coded in the HR model.
• The HRACF detects the uncertainties through space and time during physical interaction.
The remaining manuscript is organized as follows: Section 2 comprises various background studies related to the human-robot interaction to
detect the motion. Section 3 elaborates the proposed HRACF to allow the interaction between humans and robots to detect the motion and rigid-
ity of arm muscles during physical interaction. Section 4 constitutes the results that validate the time of the robot to meet the rigidity specifica-
tion. Finally, the conclusion with future perspectives is discussed in Section 5.
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2 | BACKGROUND STUDY ON THE HUMAN-ROBOT INTERACTION TO DETECT THE
MOTION
This section discusses the researchers' several works; Peternel et al. (2019) developed a selective muscle fatigue management (SMFM). A master-
learning technology is used to discover the complicated correlation between human muscle activity, arm design, and endpoint strength offered by
an advanced offline musculoskeletal prototype. SMFM is mainly advantageous that would run online, and all readings by the robotic sensory
scheme can be executed, which can considerably enhance the validity in real situations.
Li et al. (2019) introduced a novel real-time parallel variable-stiffness control (NRPVSC). The technique forecasts the body's muscle rigidity
throughout the interaction process and then changes the Series elastic actuators port rigidity as required for muscle mass training. The observa-
tional results show NRPVSC is focused on muscle rigidity forecasting that can precisely complement the port rigidity and offer stability in commu-
nication for useful muscle exercise.
Varghese et al. (2020) proposed bio-inspired kinematic sensing (BKS). BKS presents a detecting framework for an exosuit shoulder of several
degrees of freedom that senses the Kinematics of the jaw. Influenced by the body's personified Kinematic detecting, the proposed sensing device
organizes muscles and muscle efficiencies to account for moving shoulder in the IoT platform. The error rate of ≈ 5.43
and ≈ 3.65
for azimuth
and steepness combined perspectives of over 29,500 frame of motion-capture data have been evaluated for the extracted modelling.
Khoramshahi and Billard (2020) discussed a dynamical system approach (DSA). DSA provides a consolidated robotic structure for the lead role
and follower role and offers human supervision in both roles. Without advice from humans, the robot does its task independently, and the robot
actively obeys the human motions when these guidelines are detected. DSA had been evaluated in terms of monitoring and enforcement with
obedience and assessed using a six-dof manipulator empirically.
Zhong et al. (2020) developed hand-eye calibration method (HECM). HECM is implemented independently to modulate a robotic device con-
cerning a monocular camera without recognizing items or outstanding measurement characteristics. The HECM uses interactive manipulation to
monitor its rigid-body motion behaviour under the distant mixing limit. The outcomes of the da Vinci Research Kit computations and tests are
illustrated using the HECM suggested using a similar laparoscopy set-up.
Heunis et al. (2020) narrated the advanced robotics for magnetic manipulation (ARMM). In this article, ARMM developed the integration of
several sub-systems so that an autonomous operating system can be extended for clinical feasibilities to meet the duration of catheterization
challenges. The results show a mean error of 2.09 ± 0.49 mm among the catheter tip recorded and the specified location. The average time for
achieving goals is 32.6 s.
Chitalia et al. (2020) introduced fibre bragg grating-based shape sensing (FBG-BSS). In the proposed framework FBG sensor with a nitinol
tube is mounted on one edge due to the welding of its centerline. Finally, as evidence of the idea, by incorporating tendon strength comments
and FBG strain responses, the viability of a sensor assembly in generating accurate joint angle forecasts for a mesoscale robot has been shown.
Sivaparthipan et al. (2020) proposed an innovative and efficient method (IEM). The proposed IEM system for Parkinson's disease with artificial
intelligence and IoT can significantly increase its gait effectiveness. IEM clarifies the responsibilities and the interaction of robots in sickness and
big data analytics. The robot's key responsibility is to forecast movement and to give the patient strength exercises.
Gao et al. (2020) introduced pneumatic muscle actuators (PMAs) to feature of variable rigidity is coupled with a math analysis based on the
geometry and performance testing for the stiffness of each PMA forms the rigidity model to provide detailed analysis. Furthermore, the dual seg-
ment manipulator is indeed able to implement independent unit locking using the pressure variation of the PMAs in each unit.
Based on the survey, several issues regarding motion and muscle rigidity are detected in the healthcare sector. HRACF has been developed
with humans and robots for retrieving the arm muscle rigidity during the activity demonstration.
3 | HUMAN-ROBOT APPROXIMATION CHARACTERISTICS FRAMEWORK
The physiological interaction is developed among the human and robots. The robots gather the resistive abilities of muscles from the human pres-
ence. The signals are collected from electromyography and extracts the arm muscle rigidity at the time of activity demonstration. The evaluated
muscle rigidity is then linked to the robot impedance regulator. The features of motion and rigidity are modelled by an estimation and approxima-
tion model with an LR. HR uses an optimized resistive approximator to evaluate the robot's innovative variables and track the mentioned path-
ways when interacting with humans. IoT offers healthcare applications for the benefit of patients, families, doctors, hospitals, and insurers. IoT for
patients – devices such as fitness bands and other wirelessly connected devices such as blood pressure and cardiac monitoring margins, glu-
cometers, and so on… are used for validating the patient's health. An HR model sequentially codes the relationship between motion data and
rigidity data. The complete architecture of HRACF with the data collected from electromyography for detection of the motion and muscle rigidity
presentation is obtained by estimation and approximation model is illustrated in Figure 1. The data collected from human muscle is correlated with
the robotic impedance regulator by the HR model, and the final stage of the muscle rigidity is related to the robot execution. IoT devices collect
the motion of muscles and can identify the rigidity of muscles.
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A presentation scheme of human-robot parameter impedances allows robots to communicate with their surroundings. The presentation
scheme helps to adjust the human trainer's arms impedance attributes effectively without detecting the strength of the robotic arm. HRC helps
in encoding both the identified paths of moving and the rigidity profiles, taking systematically into account the connection among physiological
data and biological data. The development of the rigidity features relies on the location information obtained by IoT device, which would
enhance the efficiency of the activity of the robot. Robots are becoming increasingly common in everyday environments, from factories to hos-
pitals. People improve human capacity by developing machine intelligence, which enables robots to work together with people as very effec-
tive co-workers.
3.1 | Retrieval of rigidity
3.1.1 | Human arm geometric impedance prototype
In particular, throughout interactions between humans and robots, the dynamic response of the human arm is generally defined as a physical
impedance related to the intended strength. The dynamic response of the arm is described below
S ¼
JgaþKgbþLbc
KgbþLbc

ð1Þ
The dynamic response of the arm is obtained from Equation (1), here S represents the intended strength, and the extremity end destination to a
variety of locations is denoted as a,b,c. Jg is described as the friction of the arm, Kg represent the rigidity of the arm, Lb Denote the ending point
of the geometric impedance matrix elements of the arm with the IoT device. Robots are not only great for patients and can lead to fewer strains
for physicians and surgeons. That's because the operations are not so long or so tired without the help of a robot, however, and surgeons spend
less time under surgery and are in an uncomfortable place.
The dynamic response of the arm collected from the human arm with the various locations a,b,c is used to determine the intended strength
with the geometric impedance elements Jg,Kg,Lb is shown in Figure 2.
By neglecting the minimal impact of the dispersion of muscle strength in the proximity of the predetermined pose, a decrease in the friction
period in the input signal in the above Equation can be given as Jga ¼ 0 and the predefined stage can be denoted as KgbþLbc. The ending point
rigidity ratio Lb of the arm muscles and the modulation vector Kg is shown below
Lb ¼
Lq þLqs
Lsq þLs

Kg ¼
Kq
Ks

8

:
9

=

;
ð2Þ
The ending point rigidity ratio and the modulation vector is obtained from Equation (2), the forces Lq are related to directional characteristics, Lqs
can be related to cyclic account factors, Lsq is the representation of directional account motions and Ls represent the rotary account motions. Kq
rigidity is evaluated focusing on the electromyography signals derived from the leg of the human trainer Ks. The strength of the terminal robot
could thus be included as a result of the rigidity regulation. Human-Robot Interaction is a research area devoted to comprehending, designing,
and evaluating robotic systems for human use and use. Interaction requires communication between robots and people by definition. Social inter-
action encompasses social, emotional, and cognitive interaction aspects.
FIGURE 1 The architecture of HRACF
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3.1.2 | Electromyography based arm rigidity collection
The rigidity configuration is designed to facilitate the robot to gain knowledge from the human educator presentation about the impedance
enforcement method by IoT device. For this purpose, the rigidity of the human educator arm is first predicted. The relationship among human arm
ending point rigidity and combined rigidity is described below
Lb q,a
ð Þ ¼ H
a
ð Þ LH q,a
ð ÞMH a
ð Þ
½ þH
a
ð Þ
MH a
ð Þ ¼
dH a
ð Þga
da
þ
dH a
ð Þ
da
LH q,a
ð Þ ¼ b q
ð ÞLa
H
9

=

;
ð3Þ
The ending point rigidity and combined rigidity Lb q,a
ð Þ is obtained from Equation (3), MH a
ð Þ and LH q,a
ð Þ are the geometric rigidity structure and
H
a
ð Þ represent the mutual rigidity matrices of a human educator with q and a indicating, respectively, muscle and the combined angular template.
H a
ð Þ is the singular matrix of a human arm. In the availability of exterior forces dH a
ð Þ and gravitational stack ga takes into consideration of arm's
configuration da. b q
ð Þ is an eventually presented differential constant. La
H is the lowest joint rigidity.
It is sensible even though the stimulation of the human arm muscle can be seen automatically between various oppositional groups. In the
HRACF, the oppositional shoulders and lower back muscles are used to calculate the following muscle rigidity measure as described below
q ¼
1
V
X
V1
L¼1
CB sL
ð Þþ
X
V1
L¼1
CS sL
ð Þ
!
ð4Þ
The muscle rigidity measurement is obtained from Equation (4), here V represent the default scale of the screen. The gripped electromyography
signals of shoulders and lower back muscles are indicated by CB sL
ð Þand CS sL
ð Þ, respectively. L represent the parameters used to measure
rigidity. The arm muscle motion with the various location a, b, c with the electromyography signals of shoulders and lower back muscles are indi-
cated by CB sL
ð Þand CS sL
ð Þ is illustrated in Figure 3.
In the method of human arm endpoint rigidity, the muscle rigidity predictor is then mounted by the correlation and the data obtained from
IoT as shown below
d q
ð Þ ¼
α 1þaα2q
ð Þ
1aα1q
ð5Þ
The correlation coefficient d q
ð Þ determine the muscle rigidity predictor is determined from Equation (5), here α1 and α2 are predetermined consis-
tent constants that influence the magnitude of human arm muscle and form of d q
ð Þ.
The normalized human arm final point rigidity is consequently plotted to the Ls robotic arm ending point rigidity and is described below
FIGURE 2 Human arm geometric impedance prototype
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L ¼
L s1
ð Þ
ifLs L s1
ð Þ
αLbifL sþ1
ð Þ
s Ls L s1
ð Þ
s
(
ð6Þ
The ending point rigidity is obtained from Equation (6), here L sþ1
ð Þ
s and L s1
ð Þ
s denote the robotic arms ending point rigidity with the pre-defined
fixed point Ls that enables the robot to operate at the recommended distance Lb with an ending point rigidity L s1
ð Þ
. The robotic arm ending point
distance that can operate at the recommended distance is illustrated in Figure 4(a).
3.2 | Estimation and approximation model
Provided a series of presentation X, every presentation is denoted as x 1………X
f g comprises of recordings Shigh
indicating robotically moving
paths in the joint sphere and rigidity retrieved from the arm of the teacher. The model with L state is shown below
; ¼ ha,b
ð ÞL
b¼1,b ≠ a,αa,βH
a ,γH
a ,βa,γa
h iL
a¼1
ð7Þ
FIGURE 3 Electromyography based arm rigidity collection
FIGURE 4 Human arm and robotic arm ending point distance calculation
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The L state modelling is obtained from Equation (7), here αa is the a-th state original possibility, ha,b represent the possibility of transformation
from location b to a, γH
a ,γa represent the medium attributes and variations. βH
a denote the template modulation allocations of the L gradient period.
βa,γa are average matrix frames and expected valuesof the likelihood with L Linear interpolation under combined analysis. The linear interpolation
is obtained by the number of states and series of presentations. The linear interpolation achieves the changes in states for the template of modu-
lating allocations and the medium attributes, and the state likelihood function is shown in Figure 5.
The performance of the a-th state likelihood function with the certain condition is described below
qH
a s
ð Þ ¼ M s;βH
a ,γH
a

qa Vs
ð Þ ¼ M Vs;βH
a ,γH
a

ð8Þ
The performance of the a-th state likelihood function qH
a s
ð Þ with the certain condition qa Vs
ð Þ is obtained from Equation (8), βH
a denote the tem-
plate modulation allocations, γH
a represent the medium attributes, M represent the number of stages, s denote analysis function, Vs represent the
likelihood function with a certain condition.
The short allocation is initially updated across the estimation and approximation model. The provinces on which the system transient variables
like dimensions, velocity, and rigidity characteristics in joint area are calculated. The summary shows the likelihood with the limited interpretation
Vs is in the state a at point s is described as shown below
ga,s ¼ Q rs ¼ a;Vs
ð Þ ¼
ha,s
P
L
l¼1
hl,s
ð9Þ
The likelihood with the limited interpretation ga,s is obtained from Equation (9), here ha,s represent the initial parameter, Q is the quality factor of
the system model, rs ¼ a represent the limited values, ha,s denote forward parameter. The initial parameter ha,s is calculated as shown below
ha,s ¼
X
Llow s1
ð Þ
h¼1
hb,s1haqbH s
ð Þþ
[
t¼lsM Vs;βH
a ,γH
a

ð10Þ
The initial parameter calculation is obtained from Equation (10), here h represent the initial parameter condition, Llow s1
ð Þ denote the lowest
values in calculation stages, hb,s1 are the values in the interpretation stage, ha denote the number of parameters, qb denote the certain condition
in the interpretation stage, H s
ð Þ is the variable dimension range, Vs represent the likelihood function with a certain condition. M is the number of
stages, s is the analysis function, t is the time taken for interpretation, l is the length of the allocation stages, βH
a represent the template modulation
allocations, γH
a represent the medium attributes.
FIGURE 5 Medium attributes, and state likelihood function
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3.2.1 | Logistic regression
Every other step s is used to calculate the required process variables. The entire regression state of being focused on the fixed point as described
below
Vs ¼
P
L
a¼1
ga,s αV
a þ
P
V
a
P
V
b
1
Vs αV
a

#
Lb,s ¼
P
L
a¼1
ga,s αlb
a þ
P
lbV
a
P
V
b
1
Vs αV
a

#
8

:
ð11Þ
The motion and the rigidity features throughout the replication Lb,s is obtained from Equation (11), depending on the references Vs, expected vari-
ables in the regression status and the variations of such factors ga,s are calculated, the preferred motion αV
a and required rigidity characteristics αlb
a
are calculated by logistic regression. The initial parameter a anyway relies on the locations noted lbV, that further indicates the advancement of
the regression states Vs αV
a

and do not rely effectively on the motion or rigidity features
P
V
b
1
throughout replication. The estimation and
approximation model with the initial parameter estimation ga,s,ha,s, and the LR Lb,s to determine the location lbV is shown in Figure 6. The dimen-
sion, velocity, and rigidity correlation with the impedance regulator determine the robotic arm execution.
The logistic regression models are being used to convert situations based on motion and the rigidity of the arm muscles. The rigidity is
accepted gradually according to the development of the path of the situation. The regression model is compatible with the knowledge, which
means adjusting arms rigidity to complete a mission centered on positioning systems and not adjusting it according to motion intensity. In particu-
lar, rigidity adjustment must be effectively affected by the performance and not with the motion. With the aid of the mobility solution and other
new technologies, IoT can automate patient care and the next-generation healthcare facilities. In healthcare, IoT facilitates interoperability, com-
munication between machines, and exchange of information and data movements to ensure effective delivery of healthcare services.
3.2.2 | Robot impedance regulator
For the regulation of the robotic arm in mutual area, a differential rigidity monitoring device is used for distinct degrees of freedom. The regulator
device is implemented and defined below
Xreq Lb Vreq Veva
½ þAb Veva Vreq
½ þXintg ð12Þ
The implementation of the regulator device is obtained from Equation (12), here Lb indicates the vertical constraining vector Ab with components
on the primary vertical space that is measured for damping the regulator. The vector of the robot arm is combined with the roles of required Vreq
and evaluated Veva arm motions are along with V. The interactive strength of the muscle rigidity is represented as the internal forces Xint. The
FIGURE 6 The dimension, velocity, and rigidity correlation stages
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entire stages of motion and rigidity of muscle from the human educator and the robot impedance regulator are described below in the approxima-
tion and the impedance regulator (AIR) algorithm
The complete retrieval process of rigidity, the estimation, and the approximation model is described in the AIR algorithm. In the above
section description, the undefine complexities are not estimated to overcome that situation human-robot communication is described in the
below section
3.3 | Human-robot interaction stage
In this segment, the fundamentals of humans and the arm muscle are first introduced. As there are differentiation strategies of the human-robot
system, it is proposed to approximately estimate unidentified complexities by the optimized resistive approximator. A perfect optimized technique
is presented to achieve the exchange of cognitive abilities from a human to a robot, and an error matches the exact measurement to the optimized
techniques.
3.3.1 | Optimized resistive approximator
The characteristics of human beings and exoskeletal robots in the shoulder joint can be modelled on external force as described below
G P
ð ÞP
þQ P,P
ð ÞP
þS P
ð Þþτa ¼ τ ð13Þ
The characteristics of human and robots are modelled by Equation (13), here P,P
ϵTm
indicate the optimized model rotation co-ordinate, and
n refers to the degree of rotation; τaϵTm
indicated by the input power friction variable implemented at the muscles, G P
ð ÞϵTmm
denote the friction
grid of the positive linear values, S P
ð Þ represent the linear favourable friction matrices, τ represent torque sequence of the atmosphere
The resistive approximators are used in the development of the control unit due to the complexities of process variables. The prolonged fea-
ture may be estimated by a category of sequentially parametric feedback approximators as described below
Algorithm
Approximation and the impedance regulator (AIR) algorithm
Input Motion of arm muscle from signals gathered from electromyography
Output Robot execution to detect the muscle rigidity
Initial stage
Set the condition for ending point rigidity ratio Lb and the modulation vector Kg
Configuration of data from the combined rigidity Lb q,a
ð Þ
Muscle rigidity measurement: q ¼ 1
V :
ð Þ
The muscle rigidity predictor d q
ð Þ, rigidity retrieved from the arm muscle with L state
qH
a s
ð Þ ¼ M s
ð Þ
qa Vs
ð Þ ¼ M Vs
ð Þ
ga,s ¼ :
ð Þ is the limited interpretation for detecting muscle rigidity
ha,s ¼ :
ð Þ
f g is the parameter estimation stage
For L=3, M do
Compute
P
V
a
P
V
b
1
Vs αV
a

for the motion features extraction
End for
For A=3, K do
Compute
P
L
a¼1
P
lbV
a
P
V
b
1
Vs αV
a

for rigidity feature extraction
End for
If (X=1)
Implement using Vreq,Veva
End if
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φ V
ð Þ ¼ ;S
H V
ð Þþϑ V
ð Þ ð14Þ
The parametric feedback approximators are obtained from Equation (14), ;S
represent the control unit, H V
ð Þ represent the complexities of the
variables, ϑ V
ð Þ denote the category of the feature. The muscle motion obtained from the regulator device and the communication between the
human educator with the IoT device is modelled using the HR framework, as shown in Figure 7. The robotic arm execution is collected by human-
robot interaction.
The strengthening activities of input parameters are recognized for the ongoing base matrix of approximator features. Flexible vector thick-
ness indicates the versatile set's limiting error in the approximation method with a favourable constant. The fundamental approximation equation
shows a predictable, user-defined approximator with constant accuracy over a modular set that can estimate any linear combination. The resistive
approximator is specifically in the joint area and can be described as the next preferred resistive framework that signifies the preferred density,
vibration, and rigidity. The optimized resistive approximator is carried out by recognizing the active rigidity with the creative variables to monitor
the quoted pathways at communication time. The motion obtained from IoT devices and muscle rigidity can be obtained from human-robot
interaction.
4 | RESULTS AND DISCUSSION
The proposed HRACF has been evaluated by a testing system focused on the robot. The arm detective system is used to demonstrate the abilities
of signals collected from electromyography. The patches (IoT devices) are used to collect electromyography signals from the arm of the educator.
The collected signal is divided into the number of presentations that can be processed for rigidity identification. The processed electromyography
signal is then gathered at 200 Hz and sent for rigidity prediction to the system requirement.
The convergence rate created is then transmitted to the robot at 100 Hz. The armed robot has seven flexibility rates. A robotic device physio-
logically links the expert arm to the instructor's hand. The rigidity of arm muscle is evaluated based on the presentation of the electromyography
of the signal obtained by IoT devices. The parameters used for the validation of the proposed HRACF are shown in Table 1.
The main activity for arm muscle rigidity identification is obtained by recording the activity based on pushing the button. The rhythm of high
and low values of muscle rigidity is established by the button-pushing test that can be denoted as Lhigh
,Llow
. The collected electromyography signal
is processed with a window area of 20 ms and 40 ms. A set of window range presentations is made and trained in the proposed framework. For
the knowledge of observable factors, the number of areas of state L is selected properly and can be represented as ys ¼ VS
s ,
c
VS
s
S
,ys ¼ VS
s ,
c
VS
s
s
.
The presentation based on the signal collected and the different stages for window area 20 ms is shown in Figure 8(a), and the window area
40 ms is shown in Figure 8(b). Here different stages are evaluated based on the rigidity and motion of arm muscles are obtained by IoT devices.
By taking the rod from the robot arm and shifting it to the switch, the human educator can learn arm muscle rigidity and push buttons with
the integrated robot features. Humans showed their ability to effectively collect electromyography signals by using arm presentation from IoT
devices. The initial parameters have been forecasted using the estimation and approximation model. The rigidity dependent variable has been dis-
covered from proven rigidity characteristics using HRACF. The presented and studied rigidity of arm muscles in motion accounts is retrieved using
LR shown in Figure 9(a). The presented and measured rigidity of arm muscles are retrieved and illustrated in Figure 9(b).
FIGURE 7 The interaction between human-robot
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The preceding estimation to calculate the prediction error with the slope and motion of arm muscle in an attempt to confirm the accessibility
of the framework As. The acceleration of the motion can be projected as shown below
A ¼ Gb
PþMPþRþAs ð15Þ
TABLE 1 The parameters of HRACF
List of parameters Measured values
Degree [Rad], Window area = 20 ms, 40 ms 95
Rigidity [Nm/rad] 98
Error rate (%) 0.15
Dimension (m) 0.45
Velocity (m/s) 1.45
FIGURE 8 (a) Degree of rotation (rad) window area = 20 ms. (b) Degree of rotation (rad) window area = 40 ms
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The acceleration of motion of the arm is obtained from Equation (15), here G ¼
x1 þx2 þx3P1 þX3P1
x2 þx3P2

; M ¼ x3P2 þx3 P1 þP2
ð ÞP2
f g;
R ¼
x4P1 þx5 P1 þP2
ð Þ
x5 P1 þP2
ð Þ

. x1,x2,x3,x4,x5 represent the motion of various stages of arm muscles. P1,P2 represent the prediction stages of error in
the detection rigidity of arm muscles. The error rate prediction for each muscle varies according to the window area with 20 ms is shown in
Figure 10(a) and window are 40 ms is shown in Figure 10(b).
The parameters of connection among the motion and rigidity of muscles are varied according to the stages. The rigidity of muscle identifica-
tion for different stages is illustrated in Table 2. Rigidity has been modelled with the estimation and approximation mode. Dimension, velocity,
rigidity have been modelled separately under the five stages of motion, meaning that the rigidity adjustment is distinct from motion information.
The HRACF is used for both observations. LR can therefore achieve the relationship between both velocity and rigidity. The robot was indeed
monitored with different impedances under the acceleration control technique.
In HRACF, source pathways are approximated from the five stages of public protests necessary for the arm motion and are no longer used
during activities. The error rate for each stage shows each joint's location instructions are discovered from the presentation. The various stage
presentation shows that HRACF method can produce decent instructions, though its various demonstrations differ widely. The error rate for vari-
ous stages of muscle rigidity identification is shown in Table 3.
FIGURE 9 (a) The rigidity of studied data. (b) The rigidity of measured data
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FIGURE 10 (a) Error rate window area = 20 ms. (b) Error rate window area = 40 ms
TABLE 2 The rigidity, dimension, velocity of muscle identification for different stages
Stages Dimension (m) Velocity (m/s) Rigidity(nm/rad)
Stage1 0.78 2.45 94
Stage2 0.45 3.56 96
Stage3 0.77 4.67 90
Stage4 0.98 1.45 87
Stage5 0.66 2.78 98
TABLE 3 The error rate for different stages
Stages Error rate (20 ms) Error rate (40 ms)
Stage1 0.98 0.99
Stage2 0.45 0.56
Stage3 0.66 0.78
Stage4 0.77 0.97
Stage5 0.86 1.34
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The proposed HRACF achieves the highest rigidity of muscle identification and less error rate when compared to other existing The ARMM,
BKS, and FBG-BSS.
5 | CONCLUSION
This article presents HRACF for physiological communication between human beings and robots. HRACF allows robots to recognize the different
resistive capacities of humans' muscles. The electromyography signals are used throughout the activity demonstration to retrieve the muscle rigid-
ity of the human arm. Motion and rigidity features are simultaneously modelled using a logistic regression with estimation and approximation
model. The tested rigidity of the human arm is then linked to the robot impedance controller. HR model uses an optimized resistive approximator
to evaluate the robot's creative parameters and track the referenced pathways during the interaction. HR model is systemically programmed for
the connection of motion data and rigidity data collected from IoT devices. HRACF allows space and time to identify uncertainties that allow the
robot to comply with 98 [Nm/Rad] muscle rigidity identification and 0.15% error during the physical interaction process.
CONFLICT OF INTEREST
The author declares that there is no conflict of interest.
DATA AVAILABILITY STATEMENT
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
ORCID
Venu Madhav Kuthadi https://orcid.org/0000-0001-9295-0860
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AUTHOR BIOGRAPHIES
Rajalakshmi Selvaraj, Senior Lecturer, Department of CS IS, Botswana International University of Science and Technology, Palapye,
Botswana.
Venu Madhav Kuthadi, Associate Professor, Department of CS IS, Botswana International University of Science and Technology, Palapye,
Botswana.
S. Baskar, Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, India.
How to cite this article: Selvaraj, R., Kuthadi, V. M., Baskar, S. (2022). Human muscle rigidity identification by human-robot
approximation characteristics framework on internet of things platform. Expert Systems, 39(6), e12824. https://doi.org/10.1111/exsy.
12824
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