Tele-operation System with Reliable Grasping Force Estimationto Compensate for the Time-varying sEMG Feature

MinKyu Kim, Jaemin Lee, and Keehoon Kim

Abstract— This paper presents a real-time framework fortele-manipulation by using sEMG signals to estimate bothhuman motion and force intention. Our previous study showedthat the ability to detect discrete force levels was not applicableto complex tasks such as grasping, holding, and manipulatingvarious objects with variable force. Consequently, we iden-tified the need to simultaneously track the arm and handconfigurations and estimate the grasping force. However, it isdifficult to continuously estimate the grasping force because ofthe time-varying nature of surface Electromyogram (sEMG)signals, even if a force remains constant. To solve such aproblem, this study proposes a new regression strategy to enablecontinuous and proportional measurements and transmissionof the grasping force by using sEMG signals in transient andsteady-states. A 7-DOF robot arm with a robotic hand was ableto remotely imitate a subject via an easily-wearable sEMG andinertia measurement units sensor interface. The experimentalresults verified that the motion and force capturing system suc-cessfully enabled interaction tasks, such as grasping, holding,and releasing motions with objects, with reliable and continuousforce estimation.

I. INTRODUCTION

In human robot interaction (HRI) applications, providingfully-automated motion in unstructured environments has notyet been achieved, thereby requiring the need for humanoperators to deliver motion instructions to robots for themto be properly operated within their surroundings. As aresult, various studies on haptic devices, optical marker-based tracking systems, and inertia measurement unit (IMU)sensors have been widely reported to generate well-plannedmotions.

Recently, EMG-based systems have also been imple-mented to operate robots at a kinematic level or to classifydiscrete motions with pattern recognition to track humanarm motion [1][2]. Surface electromyogram (sEMG) signals,which contain muscular information, directly reflect the bothhuman motion intention and force information. Capturinghuman force is one of the distinctive characteristics of sEMGsignals that the other sensors mentioned above do not have.Thus, exploiting this bio-signal permits human force to be

*This work was supported by the convergence technology developmentprogram for bionic arm through the National Research Foundation ofKorea(NRF) funded by the Ministry of Science, ICT & Future Plannig (No.2014M3C1B2048419) and the Global Frontier R&D Program on Human-centered Interaction for Coexistence funded by the National ResearchFoundation of Korea grant funded by the Korean Government(MSIP) (NRF-M1AXA003-2010-0029748).

M. Kim, J. Lee, and K. Kim are with Korea Institute of Scienceand Technology, 39-1, Hawolgok-dong, Seoul, Republic of Korea (e-mail:{stevemin,jmlee87,khk}@kist.re.kr). K. Kim is the corresponding author.

Fig. 1. Tele-operation of K-Arm with visual feedback

effectively delivered in powered prostheses, HRI, or tele-operation applications. [3][4][5].

In a previous study, we proposed a real-time motion andforce capturing system for tele-manipulation based on sEMGsignals and IMU motion sensors [6]. We confirmed that thefusion of sEMG signal with IMU sensors could be employedto track human arm motions and we successfully classifiedtwo types of grasping postures (the power grasp, and thepinch grasp). However, we were unable to quantitativelycalculate force amount because the previous system exclu-sively determined the discrete force level based on the signalduration. This force detection method could not accommo-date dexterous manipulation in dynamic conditions such asobject weight changes or the sudden release of objects. Theability to quickly and precisely detect continuous humanforce exertion is required to resolve these dynamic tasks.

To estimate the precise magnitude of muscular forcesfrom sEMG signals, numerous techniques have been utilizedsEMG signals to estimate the torques of human elbows [7],wrists, and knee joints. Alternatively, grip forces have beenused [8] in place of F/T sensors or encoders for rehabilitationand tele-operation purposes [9][10]. Estimated finger or wristtorque values from sEMG signals have been successfullydelivered to prosthetic hands or exoskeleton robots in orderto control their motion [11][12]. In addition, several forceregression algorithms have been studied [13][14] to identifyoptimal linear and non-linear regression models with Arti-ficial Neural Networks (ANN) [15][16], Locally WeightedProjection Regression (LWPR) [17], and other regressiontechniques [18].

These regression techniques provided satisfactory perfor-

2016 IEEE International Conference on Robotics and Automation (ICRA)Stockholm, Sweden, May 16-21, 2016

978-1-4673-8026-3/16/$31.00 ©2016 IEEE 5561

Arm Tracker (Section - B)

Raw

Signal

Pre

processing

Learning

Classifier

Feature

Extraction

Regressor

Hand Tracker (Section - C, E)

Pre

processing

Calibration

joint

Calculator

Calibration

Calculation

IMU

EMG

Human

Robot Control (Section -B)

Desired

Hand

Motion

Desired

Force

PD

control

Visual

feedback

Desired

Arm Motion

Trajectory

Training

Classifying

IMU

Sensor

Force

Controller Robot

Fig. 2. The whole framework of the integrated system

mances; however, they were limited to transient-state appli-cations only. This was because, using this technique, most ofthe errors occur when the signal varied over time. Therefore,the time-transition property during the task could cause adeviation from the regression model. Taking this into consid-eration, the regression model must adapt to the time-varyingcharacteristics of EMG in order to minimize errors. Focusingon grasping, holding, and releasing tasks for robot hands,we proposed a regression strategy for continuous trackingof grasping force variation. A regression model was usedas a logarithmic function. Traditionally, this model fits wellto transient sEMG signals that are measured from humangrasping forces. In this study, a new regression strategy isproposed to compensate for steady-state regression errors.

The goal of this study is to establish a simultaneous andproportional control framework for multi-functional taskswith human arm and hand motions by using wearablesEMG/IMU sensors for a long period of time. A 7-DOFarm manipulator was used to validate the proposed mo-tion and force capturing system for two types of graspingpostures (pinch and power grasping). Through real-timeoperation, a robotic manipulator successfully grasped nu-merous objects with different postures and calculated forces.The proposed regression model successfully circumventedthe time-transition regression error that is characteristic ofsEMG signals. This proposed system made it possible tocontinuously predict the grasping force with both transient-and steady-state signals.

This paper is organized as follows. Section II describesthe human grasping motion analysis method, and SectionIII presents the detailed pattern recognition algorithms, theregression model, and the overall framework. In SectionIV, the proposed methodology is validated via experimentalresults. This is followed by the last section, which containsthe conclusion and a discussion of the limitations we faced

while performing the experiments.

II. SYSTEM DESCRIPTION

This section explains the proposed framework, which con-sists of an arm tracker, a hand tracker, and a robot controllerdesigned for the real-time motion and force capturing system.The arm tracker used IMU sensors to generate desiredjoint values, while the hand tracker used sEMG signalsto determine grasping gesture and corresponding force asshown in Fig. 2. Desired commands from these trackers mustbe converted to control inputs in order to operate the robots.

A. Data Acquisition and bionic interfaces

Our data acquisition system is a small, easily wearableinterface, containing wireless IMU sensors (EBIMU24GV2,E2BOX Co.) and sEMG sensors (Trigno wireless, DelsysInc.) as shown in Fig. 3. The sampling frequency of theIMU sensor is 100 Hz and sEMG signals were acquired at a 1kHz sampling rate with the wireless communication that usesa 2.4GHz ISM band. The sEMG signals were sequentiallytransferred to the sEMG system and an A/D data acquisitionboard (S826, Sensoray Co.) on a desktop PC (Windows 7,i7-2600 3.40 GHz CPU, 8 GB RAM).

B. Arm tracker

Human arm motion was imitated by our K-Arm manipula-tor. The desired joint input for the manipulator was obtainedby the arm tracker shown in Fig. 2. To track human armmotion, four IMU sensors were attached to the torso, upperarm, forearm, and hand. The locations of the IMU sensorswere not specified, and were mounted approximately byusers. Instead, for precise tracking, a calibration process wasrequired to calculate the relative transformation from humanbody to sensor frame [6]. The detailed algorithm of the armtracker used to obtain desired joint values,or q j( j = 1, · · · ,7),is described below.

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Fig. 3. Human joints and sensor attachment locations. X : R{Si}{Pi}

, Y : R{Si+1}{Ci}

,Zi : rotation of joint i , {Si} : the body frame of sensor i , {Ci} : the childlink frame of joint i, {Pi} the parent link frame of joint i.

Zi = [SiX ]−1[Si+1Y ]

q j = iEulerZY X(AdTi(Zi))(1)

( j = (1,2,3) or (1))

where, X is the rotation from the parent link frame {Pi} to{Si} and Y is the rotation from the child link frame {Ci}to the sensor frame {Si+1} as shown in Fig. 3. Zi equals tothe rotation of joint i and {Si} is the body frame of sensori. {Ci} , {Pi} are the child link frame and the parent linkframe of joint i, respectively. The sensor calibration processpre-calculated X and Y with predefined calibration pose.To obtain the desired joint value, we utilized iEUlerZY Xfunction and Ad (adjoint mapping) with the rotation matrixTi ∈ SO(3), or the relative rotation between the robot modeland sensor frame.

C. Hand Tracker: Pattern Recognition

An EMG-based pattern recognition system of the handtracker classified three target motions: two grasping postures,pinch and power grasping, and a palm-open (release motion)gesture. The proposed pattern recognition system, whichused sEMG signals, consists of four steps, shown by the handtracker in Fig. 2 : (i) preprocessing, (ii) feature extraction,(iii) classification (classifier, learning algorithm, and classassignment), and (iv) regressor.

1) Preprocessing and Feature Extraction: To extractmeaningful features, the sampled signal data required properpre-processing. This included applying a band pass filterranging from 15- 500 Hz and an absolute filter to ensurethat the properties were non-negative before extracting thetime domain feature. We utilized the mean absolute values,which is one of the most popular time domain features, as afeature for the signal.

2) Training protocol: Two types of grasp postures (thepower grasp and the pinch grasp) and the palm-open postureare were utilized in supervised learning. In the trainingsession, the visual information from target motions is wasprovided with random sequences and motion cues. Each trialhad a recording and rest equal to 3 s, and was repeated

three times to collect as much information as possible andto eliminate outliers.

3) Learning and Classification: We used an ExtremeLearning Machine (ELM) classifier because of its abilityto learn extremely quickly compared with other learningalgorithms, and the high performance has been proven inreal-time classification applications [19]. We used an RBFkernel function and 10 neurons for each class. The hiddenparameters of this classifier were independently determinedfrom the training dataset, and the output parameters can bedetermined by a pseudo-inverse method using the trainingdata. The analysis window sizes for incoming signals wasset to 50 data to reduce a classification delay [20].

D. Pilot study for Grasping Force RegressionWe performed a pilot study to obtain multi-channel sEMG

signals and force data from three healthy subjects withages ranging from 27-30 years. From this experiment, wedetermined how many sensors should be used and where toattach these sensors. Furthermore, to construct the regressionmodel, the sEMG signal-force relationship was obtainedfrom repetitive data acquisition from five trials for eachsubject

(a) (b)

Fig. 4. Sensor attachment to the target muscles; FCR, FCU, ECU, ED : (a)power grasp (posterior) (b) power grasp with mini 45 FT sensor(interior)

To measure sEMG signals with the grasping force simul-taneously during a power and pinch grasp, fourteen sEMGelectrodes and a six axis F/T sensor (Mini-45, ATI) wereused. Subjects were given 5 s to complete their hand gesturesand were required to impose a grasping force by using F/Tsensors, toward the center of the F/T sensor. This enabled usto target the following muscles: flexor carpi radialis (FCR),flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU),and extensor digitorum (ED). These muscle locations arealso considered to produce distinctive features for betterclassification performances.

Recorded sEMG signals from each sensor are defined as:

e(k) = [e1(k) · · · e2(k) · · · en(k)]T ∈ℜn×1 (2)

where ei indicates the amplitude of sEMG signal in ithsensor and n is the dimension of e, which is equals to thenumber of sensors used (four sensors in this experiment). kdenotes the time index of signal. The L2 norm of the signalvector was obtained as:

‖e(k)‖=

√n

∑i=1

e2i (k) (3)

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and it was passed through a moving average filter as follows:

‖e(k)‖= 1m

k

∑j=k−m

‖e( j)‖ (4)

where m is the window size of filter, which is larger thanthat of filtered for pattern recognition in Section II-C.1. Thereason why we use large window size is that it allows signalto be differentiable with smooth curve. In this study we setwindow size of moving average (m) as 250 data. Hereafter,the term ‖sEMG‖ is used interchangeably with ‖e(k)‖ forconvenience.

(a) (b)

Fig. 5. sEMG-force data samples for two grasping postures : (a) powergrasp (b) pinch grasp

From the data collected in the pilot study, ‖sEMG‖ andgrasping force were plotted in Fig. 5. The force range of twograsp gestures was different : the power grasp ranged from0-35N while the maximum pinch grasping force was 15 N.Using data from the pilot study, proper regression modelsfor EMG-force relationship were investigated. To evaluatethe regression error, the coefficient of determination (r2) wasused as shown below:

r2 = 1− Σi(yi− fi)2

Σi(yi− y)2 (5)

where, yi represents observed data, fi represents the regres-sion data, and y is the mean of the dataset. The range ofr2 lies between 0 and 1 and a high value of r2 meansthat the regression has been well-fitted to the dataset. Forthree healthy subjects, the mean and standard deviationvalues of the coefficient of determination were compared todetermine the proper regression model and the results shownin TABLE.I.

TABLE ICOMPARISON OF REGRESSION MODEL PERFORMANCE

model Regression function Average r2 STD

Linear y = ax+b 0.874 0.022

quadratic y = ax2 +bx+ c 0.913 0.015

logarithm y = alogbx+ c 0.935 0.011

The average error of the logarithmic regression model wasthe smallest amongst the linear, quadratic, and logarithmicmodel. Thus, we utilized the logarithmic regression model

for the correlation between sEMG and grasping force values[21]. The resulting regression model could be represented asfollows:

f = R(‖e(k)‖) = a logb(‖e(k)‖)+ c (6)

where a, b, and c are parameters of the regression model fordata fitting, and depend on the classification result (Ck).

E. Regression model

||sEMG||State

Discriminator

Regression

Model

CkMotion

Classifier

S k

e(k)

Fig. 6. Regression block diagram

Fig. 6 describes the flowchart for force regression. Oncethe obtained ‖sEMG‖ arrives at the state discriminator block,it judges the signal state with the differential value of the‖sEMG‖ as shown in the following equation :

∆‖e(k)‖= ‖e(k)‖−‖e(k−1)‖ (7)

Then, the state discriminator checks both the sign of thedifferential value and the zero-crossing rate (ZCR) to eval-uate the state of the sEMG signals [22] [23]. ZCR can becalculated as:

ZCR(k) =∑

kj=k−N+1 |sgn[∆‖e( j)‖]− sgn[∆‖e( j−1)‖]|

2N(8)

where sgn[x] ={ 1 x≥ 0

0 x≤ 0

If the sign is positive and ZCR has a below the thresholdvalue, then the signals belongs to transient-state, otherwisethey belongs to steady-state. The precise discriminator func-tion works as:

Sk ={ 1 ∆(‖e(k)‖)> 0 & ZCR(k)< ε

−1 otherwise (9)

where ε indicates the threshold value for determiningwhether the number of slope changes (ZCR) is significant ornot. This discriminator determined the mode output, or Sk,as transient-state only when the signal was increasing andthe slope changes were scant. Consequently, our proposedmodel was constructed with this mode output and regressionmodel of transient and steady-state as:

R(x) =1−Sk

2R(x)+

1+Sk

2R(x) (10)

where Sk is the output mode from state discriminator, whichvalues are (1,-1) and R and R denote transient-state andsteady-state regression curve, respectively. We used the first

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Forc

e

Dead zone ||sEMG||

G

Fig. 7. Proposed regression model. δ represents the critical threshold thatdetects the activation of the signal

gradient of the transient regression model to create a steadyregression model as shown below:

R(x) = R′(x∗)(x− x∗)+R(x∗) (11)

R′(x∗) =∂ R

∂ |e(k)‖

∣∣∣∣x∗

(12)

where x∗ denotes the last detected extreme point, also definedas the local maximum in the time-sEMG domain, whichsatisfies the following condition : d‖e(k)‖

dt (x∗) = 0Thus, the tangent line passing through this point

(x∗, R(x∗)) becomes the local regression model for steadystate, or R as shown in Fig. 7. Then, substituting Eq. (11)into Eq. (10), the final model can be represented as

R(x) =1−Sk

2R(x)+

1+Sk

2R′(x∗)(x− x∗)+ R(x∗) (13)

and this equation varies with respect to parameters Sk and x∗.Because these parameters include a gradual trend (increase ordecrease) with momentary slope change information of thenorm of signals, our regression model can adapt the time-varying feature of sEMG signals.

On the other hand, assuming that the robot succeeds inholding objects, if the sEMG signal becomes lower than thecritical threshold δ , then the calculated force becomes zeroeven if the human still holds the object. To accommodatethis situation, the range of estimated forces must be set toFmin and Fmin as shown in Fig. 7.

Fmin ≤ R(x)≤ Fmax (14)

where, Fmin and Fmax are the minimum force required tohold the objects and maximum force that robotic hand cangenerate respectively. As a result, the range of proposedregression model is depicted as gray section G in Fig. 7.In this experiment, the minimum force value for the twograsping posture is 2 N. Fmax of the power grasp is equal tothe maximum payload of the robotic hand.

III. ROBOT CONTROL METHODS

All of the programs in this study were implemented inC++ language and communicated via the TCP/IP protocol.To guarantee robust timer function, the RTX (VentureCom)real-time operating system was utilized.

A. K-Arm Robotic Manipulator with an Allegro Hand

K-Arm is a robotic manipulator with 7-DOF (3-DOFfor shoulder, 1-DOF for elbow, and 3-DOF for wrist), andAllegro Hand is a robotic hand with 16-DOF. The payloadof the manipulator is approximately 50 N. Allegro Hand(SimLab Co.) is a four-fingered torque-controlled robotichand that can assume diverse grasping postures with respectto the object shape, and has a maximum payload of 50 N.It is equipped as the end effector of K-Arm. The arm andhand controller operates with a sampling rate of 1 kHz andutilizes the EtherCAT communication protocol.

B. Control Framework

1) Manipulator & Hand control: The robot control blockdiagram in Fig. 2 shows the control scheme for the K-Armwith an Allegro Hand. As the desired values of the armand hand are delivered from the arm and hand tracker thatmight contain some noise, low-pass filtered data are used forthe command signals of the K-Arm and Allegro Hand. Thecommand signals for hand motion and desired amount offorce from the hand tracker were used to execute predefinedpower-grasp and pinch-grasp hand motions, while adjustingthe grasping force proportionally. The controller was basedon a proportional-derivative (PD) control with gravity andfriction compensated for in a joint space.

τm = Kp∆q+Kd∆q+g(q)+ f (q) (15)

where τm is the control torque for manipulator, Kp and Kdare PD control gains, ∆q is the control error, g(q) and f (q)are feed-forward control inputs to compensate for gravityand friction, respectively.

2) Grasping Force Control: Open loop force controlschemes were applied to provide control inputs for handgrasping. From desired configurations and estimated forcesdelivered from the hand tracker, the desired grasping forceλc is obtained using the following equation:

λc = khR(x) (16)

where R(x) is regression model and proportional gain kh.The desired force value λc was converted into control inputτh by:

τhi = JT (λc)

Li

‖Li‖) (i = 1,2,3,4) (17)

where J is the Jacobian matrix between the grasping forceand joint torque and i represents the finger index. Li =pi− pcop represents the direction of grasping, pi is the end-effector position of each finger (i = 1, · · · ,4) and pcop is thecenter point of the fingers. In the case of power-grasp motion,we used four fingers and for pinch-grasp, we used 2 fingers.

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pi can be derived from forward kinematics with the currentjoint configuration. For each grasp, the initial grasping beganalong the predefined grasping direction.

IV. EXPERIMENTAL RESULTS

A. Arm motion tracking

The K-Arm with an AllegroHand simultaneously followedthe desired human command through an easily-wearablesensor interface with visual feedback as shown in Fig. 1.Even though there were some limitations to representing aremote environment in 2D, the novice user could experiencethe perspective via 3D display. After simple training toutilize their motion to control the manipulator, the subjectssuccessfully conducted required tasks with the manipulator,such as grasping, holding, and releasing differently sized andshaped objects.

B. Hand motion classification

1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000

0

1

2

3

4

5

6

x 10−3

Times (ms)

sEM

G (

mV

)

Ch 1

Ch 2Ch 3

Ch 4

Power grasp

Pinch grasp

Fig. 8. The experimental data for pattern recognition: (a) sEMG signalsof power and pinch grasping motions

sEMG-based pattern recognition systems and the forceregression model successfully classified grasping motionsand calculated the magnitude of the grasping force. Theclassification accuracy was almost 100% because the featuresof the three postures were completely different from eachother. Our system predicted the motion in an average of 45ms. This meant that the classification could be finished beforethe movements were enacted.

C. Grasping force regression

Continuous regression results can be seen in Fig. 9.The blue line represents the normalized sEMG signal, theblack line represents the estimated force from the proposedalgorithm, and the gray line indicates the actual sensor value.The ranges of estimated forces from the human and robotichands were different; therefore, a scaled force value wasdelivered as the control input. The proposed regression modelcan estimate a sufficient force range for each grasp posturesuch that the power grasp and pinch grasp ranges from 0-35N and 0-15 N, respectively, as shown in Fig. 9.

There were three different regression curves; case A, caseB, and case C in Fig. 9(a). The first case was the scenario inwhich the grasping force varied continuously, the second case

00

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spin

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rce

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m

normal

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rrors

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Fig. 9. The experimental data for on-line regression : (a) Real-timeregression force data : case A, case B, and case C; case A includes transientsEMG signals, while the case B has the steady state, and both states arein the case C. (b) The normalized EMG-grasping force for case A, case B,and case C. (c) Comparison of regression error for each cases

was the scenario in which a grasping force was maintainedfor a long period, and the third curve was scenario for naturalgrasping pattern that included transient and steady state. Inthe first case, a regression curve provides the precise forcewithin 3% error because the regression curve was obtainedfrom the transient-state sEMG signals. For steady sEMGsignals, we were able to verify a difference between theactual measured force data (black) and the regression curve(red), but our regression algorithm (gray) compensated forthis difference to track real force data in steady state inthe TABLE I. In real-time motion, estimated force fromthe steady state sEMG signals were not close to the realforce values. Without our regression strategy, the regressionerror were much higher, around 20%. In Fig. 9(b), we canconfirm proposed algorithm compensate regression errors

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and have a capability to well track the actual force comparedto without our strategy. The errors with and without theregression algorithm are shown in Fig. 9(c). Average errorwas normalized with the mean value of the measured forcein the experiment.

V. CONCLUDING REMARKS

This study was related to a real-time motion and forcecapturing system for tele-operated robotic manipulation thatcombined hand gesture recognition, grasping force regres-sion, and arm tracking by using sEMG/IMU sensors. A 7-DOF arm manipulator with a 16-DOF robotic hand wassuccessfully synchronized with a human arm through aneasily-wearable hands free interface with 3D visual feedbackthat was well-controlled by a novice user.

We simultaneously recognized grasping postures and es-timated grasping forces from the sEMG signals: power andpinch grasp motions, which are mostly widely used to pickup or grasp objects. Furthermore, utilizing sEMG signalscan lead to high speed performance in tele-operated systems.Human grasping forces were estimated successfully withina 9% error rate by using a logarithmic regression modelmodified to accommodate the time-varying characteristic ofsEMG signals.

There still exist several issues that need to be resolvedin the future. First, there are muscle artifacts with respectto arm configuration, as mentioned in our previous work.Decoupling the sEMG signal from the arm motion and handconfiguration is required to detect human intention accuratelyduring movement. However, while humans move their arms,determining the onset and offset of the active signals byusing a threshold filter has limitations because the systemis sensitive to the threshold change. If the threshold valueis low, misclassification errors increase because of noise orcrosstalk signals. This threshold method contains a trade-offrelationship with accuracy and speed.

Secondly, haptic feedback is required for effective inter-action between the robotic hand and the environment. At thecurrent status, only visual feedback is available, which is notsubstantial for users to know the current state of the robots.User must be able to perceive what the robot is sensing anddoing by through useful information such as contact statusor a force at the fingertip.

Lastly, grasping force control can be improved upon byattaching an F/T sensor to the fingertip of the robotic hand.With force information on contact points, force feedbackcontrol will enable more dynamic and interactive tasks.

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