Abstract—From observation of human data, a set of

sinusoidal trajectories were developed to mimic the human

motion of unscrewing a bottle cap with the thumb and index

finger. These trajectories were then implemented on a

dexterous robotic hand in the form of a robustly stable sliding

mode control algorithm. With the developed synergies, a single

myoelectric input was used to control multiple finger joints

simultaneously. This controller was then compared to a one

degree of freedom prosthetic hand also under sliding mode

control. The two hands were used to complete an unscrewing

task as quickly as possible. The synergy controller produced a

statistically significant reduction in task completion time and

also reduced the required workspace.

Index Terms—Dexterous Hands, Distributed Parameter

Systems, Electromyogram, Grasp Synergy, Prosthetic Hands

I. INTRODUCTION

he introduction of dexterous robotic hands into industry

will permit the possibility of replacing humans with

robots in complex, tedious, or dangerous tasks.

Manipulators such as the Dexterous Shadow Hand [1] and

Gifu Hand have a high level of dexterity, approaching that

of the human hand. These manipulators have suffered from

the large number of actuators required to provide such

dexterity. This shortcoming typically requires the motors to

be placed outside the hand. The motors are then connected to

the joints through a tendon routing system. A recent advance

in this regard is the SmartHand transradial prosthesis, which

contains four actuators inside the hand and is similar in size

and weight to the human hand [2]. The SmartHand possesses

16 joints actuated by four motors, and is capable of multiple

grasps. Because this manipulator is completely self-

contained, it has a large potential to be fitted to upper limb

amputees.

However, most prosthetic hands contain only a single

degree of freedom (DOF) that opens and closes in a pinch

grasp [3]. Even the more advanced prosthetic hands

commercially available today, such as the i-Limb, contain

only five active degrees of freedom (DOFs) and one passive

DOF at the thumb [4]. While this disparity between

Manuscript received April 27, 2012. This work was supported in part by

the University of Akron Faculty Research Grant FRG1708.

B. Kent*, N. Karnati, and E. Engeberg are with the Mechanical

Engineering Dpt. at the University of Akron, Akron, OH 44325 USA (B.

Kent contact; phone: 330-998-4122; e-mail: bak17@zips.uakron.edu. N.

Karnati email: nk29@zips.uakron.edu , E. Engeberg email:

engeberg@uakron.edu).

prostheses and human hands is in part due to the increased

mechanical constraints, it is also because of the limited

number of available control inputs.

Prosthetic hands are often controlled by two

electromyogram (EMG) signals placed on an antagonistic

muscle pair [5]. The signals from these two antagonistic

muscle groups are then differenced to produce a dual

polarity control signal for the motor of the prosthesis [3].

There have been many techniques proposed to improve

EMG control; feature extraction, neural networks, and

wavelet transforms have been previously utilized to classify

EMG signal patterns and obtain greater accuracy in

decoding the user’s intended movement [6]. However, these

methods suffer from their own set of drawbacks such as an

increased number of EMG recording sites and additional

time delays to process EMG signals.

Despite differences in the proposed EMG control

techniques for multiple DOF systems, the problem remains

that the mapping of control inputs to outputs is typically less

than one to one. This makes it difficult to control multiple

functions simultaneously without requiring an expensive

number of inputs or high cognitive demands from the

amputee.

The prime contribution of this paper is a synergy

controller which allows a dexterous robotic hand, described

in Section II, to produce unscrewing motions of the finger

and thumb with a single EMG input. This was accomplished

by analyzing the finger joint motions of humans performing

the unscrewing task and approximating these motions with

sinusoids, as outlined in Section IV. The proposed sinusoidal

synergy controller for the dexterous hand is derived in

Section V. For comparison, the synergy controller is

compared to a one DOF prosthetic hand, described in

Section VI. Both artificial hands were then evaluated

through a timed unscrewing task, and compared to results

obtained with the human hand. Experimental methods and

results are contained in Section VII and Section VIII,

respectively.

II. THE SHADOW DEXTEROUS HAND

The Shadow Dexterous Hand is a 24 joint, 20 DOF

underactuated tendon-driven anthropomorphic manipulator.

Hall effect sensors within the hand provide joint angle data

for all 24 joints of the hand, with a resolution < 1˚. The

index, middle and ring finger each have four joints and three

DOFs. The distal interphalangeal (DIP) joints of each finger

are kinematically coupled to the proximal interphalangeal

EMG Sliding Mode Finger Joint Synergy

Control of a Dexterous Artificial Hand

Benjamin A. Kent, Student Member, IEEE, Nareen Karnati, and Erik D. Engeberg, Member, IEEE

T

The Fourth IEEE RAS/EMBS International Conferenceon Biomedical Robotics and BiomechatronicsRoma, Italy. June 24-27, 2012

978-1-4577-1198-5/12/$26.00 ©2012 IEEE 87

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(PIP) joints. All 24 joints are driven by 20 motors located

below the wrist joints, with a pair of antagonistic tendons

connecting each motor to the corresponding joint. However,

only the index finger and thumb are used in the experiments

presented in this paper. The considered kinematic models of

these digits are given in Fig. 1(a).

Since the DIP and PIP joints of the index finger are

coupled, a virtual joint is defined as the sum of the DIP and

PIP angles. This relationship can also be described by the

following piecewise linear equations:

where is the position of the virtual joint and

x1a and x1b are the angular positions of the DIP and PIP,

respectively (Fig. 1(a)). This is a mechanical constraint

because the DIP and PIP joints of the index finger are both

controlled by a single motor. The extension/flexion of the

metacarpophalangeal (MCP) joint is x2, and

abduction/adduction of the MCP joint is x3 (Fig. 1(a)). The

system model for the seven DOFs of the Shadow Hand

thumb and first finger can be written as

where , and are matrices that

respectively contain the inertia, damping and stiffness terms

of the seven DOFs within the thumb and first finger.

is a vector of torques applied by the seven motors.

III. CYBERGLOVE II

A. Sensors

The hand motion profiles of nine human test subjects were

recorded using the ver. 2.2 22 sensor CyberGlove II (Fig.

1(b)) (Immersion Corporation, San Jose, CA). The

CyberGlove uses bend-sensing resistive technology to

convert hand motions into digital joint angle data. The index

finger of the CyberGlove contains four sensors, two of

which record joint angle data for DIP and PIP joints (FJ1a

and JF1b). The remaining two measure the extension/flexion

(FJ2) and abduction/adduction (FJ3) of the MCP joint.

For the thumb, TJ1 and TJ2 measure the flexion/extension

of the DIP and MCP, respectively. Sensors TJ3 and TJ4 of

the CyberGlove respectively record the abduction and roll

angles of the carpometacarpal (CMC) joint of the thumb

(Fig. 1(b)). Abduction of the index finger is calculated as a

weighted difference between sensors FJ3 and TJ3.

B. CyberGlove-Shadow Hand Joint Angle Correlation

The correlation of the thumb joints from the CyberGlove to

the Shadow Hand is such that sensors TJ1 and TJ2 of the

CyberGlove (Fig. 1(b)) correspond to angles x7 and x6 of the

Shadow Hand (Fig. 1(a)). These represent the

flexion/extension of the DIP and MCP joints of the thumb,

respectively. Sensors TJ3 and TJ4 of the CyberGlove are

respectively mapped to angles x5 and x4 of the Shadow Hand

(Fig. 1(a)). The mapping of the index finger from

CyberGlove to Shadow Hand is such that sensors FJ1a,

FJ1b, FJ2, and FJ3 correspond to angles x1a, x1b, x2, and x3

respectively.

IV. HUMAN HAND MOTION ANALYSIS

A. Experimental Methods

Nine human test subjects gave informed consent prior to

experiments in accordance with IRB protocol. After signing

the consent forms, they underwent a brief CyberGlove

calibration procedure. After the glove was calibrated, the test

subjects performed the following steps: first, each subject

was asked to place his or her right hand flat on the table.

After two seconds, the participant was asked to grasp the

neck of a bottle using the middle, ring, and pinky fingers.

Using only the index and thumb, the bottle cap was then

unscrewed and screwed back on. This procedure was

repeated three times for each test subject. After data

collection, the hand motion profiles of the individual trials

were analyzed in Simulink. A principal component analysis

(PCA) was performed on the data to determine the impact of

each joint during the task using the princomp function in

MATLAB. As will be further explicated, the periodic nature

of the human finger joint angles to produce the unscrewing

motion allowed the human joint position profiles to be

Fig. 2. Results of PCA analysis for individual subjects while unscrewing.

(a) (b)

Fig. 1. (a) The seven DOF kinematic diagram of the thumb and index finger

of the Shadow Hand; the DIP and PIP of the first finger are kinematically

coupled. Axes of rotation are visualized as black arrows. Axes of rotation

perpendicular to the page are designated by an (X). (b) The CyberGlove II

has 22 sensors to measure the motions of human hands.

88

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approximated well by a set of sinusoidal trajectories.

B. Principal Component Analysis Results

A PCA was performed on the data obtained from human

experiments. PCA is commonly used as a data reduction

method to eliminate redundant variables in high dimension

problems. Results of the PCA show that the first principal

component (PC) for each test subject accounts for 83% of

the variance on average. The scalar coefficients of the first

PC for each test subject were converted to percentages to

determine the contribution of each joint variable to that PC

(Fig. 2). Results show that joint FJ2, TJ1, and TJ2 have the

largest impact on the motion.

C. Sinusoidal Approximation of Finger Joint Trajectories

The joint angle data from the experiments were filtered

and normalized with respect to time. Observation of the data

revealed two tendencies: first, that the individual finger

joints exhibited a periodic motion while unscrewing the

bottle cap. Second, the frequency of this periodic motion

remained relatively constant for all joints throughout the

duration of each trial. This trend was consistent with all nine

test subjects for each finger and thumb joint (Fig. 3(a)).

Because of the periodic nature of the joint angle motions

used by the test subjects to complete the task, these joint

trajectories were approximated by sine waves. This has been

done in previous work, where the developed sinusoidal

trajectories allowed the Shadow Hand to successfully

unscrew and screw a bottle cap [7]. Due to kinematic

differences between the Shadow Hand and human hand (see

(1) and (2)), the developed trajectories for the index finger

could not be directly implemented on the Shadow Hand.

To achieve this, the forward kinematics were derived and

used to find the resulting fingertip trajectories in Cartesian

space [7]. From there, solution of the inverse kinematics

problem for the index finger produced a new set of

sinusoidal parameters which could be implemented on the

Shadow Hand (TABLE I). The sinusoid parameters obtained

from the inverse kinematics solution were then implemented

on the physical Shadow Hand, explained further

subsequently. The sine wave parameters are given in

TABLE I; these include the amplitudes, phase angles, and

offsets for each joint.

In Cartesian space, these fingertip and thumbtip

trajectories form elliptical trajectories that are periodic on 2

(Fig. 3(b)). For the purposes of the present work, the

elliptical trajectories are considered in two halves. In the

first half of the sinusoid cycle, the finger is considered to be

in the “contact stroke” of the synergy, where both the index

and thumb will make contact with the bottle cap, causing

rotation. In the second half of the cycle, the finger is

considered to be in the “return stroke” where neither finger

is in contact and no rotation of the bottle cap occurs (Fig.

3(b)).

V. SHADOW HAND SYNERGY CONTROLLER

A. Synergy Controller

By utilizing the joint synergies described above, all DOFs

involved in the unscrewing motion (or the screwing motion

[7]) can be controlled by a single input. The desired position

(xD ) for the synergy controller involving n joints is of

the form

where A is a constant diagonal matrix:

.

The quantities A1-An represent the amplitudes of the

sinusoidal trajectories for the index finger and thumb joints

(TABLE I). is a vector of sinusoids

T.

is a vector of joint angle offsets:

T.

The phase shift ( ) and joint angle offset (bK) for any joint

k, are determined from the observations of the human data

(Fig. 3, Fig. 3a) and are included in TABLE I.

(a)

(b)

Fig. 3. (a) The unscrewing finger joint motions of the nine test subjects

recorded by the CyberGlove were periodic and resembled sinusoids. (b)

Elliptical trajectory of the index finger in Cartesian space when driven by

sinusoids in the joint space. is a switching term used to map the generated

trajectories to the EMG signals of the test subject, as explained in Section

V.

TABLE I

SINE WAVE SCALING PARAMETERS FOR SHADOW HAND (RAD)

Joint x1b x2* x4 x6 x7

Gain 0.62 0.22 0.31 0.25 0.43

Phase Offset 4.712 1.571 3.66 1.571 4.712

* Reference Joint (RJ)

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Fig. 4. PID sliding mode control diagram for the Shadow Hand synergy controller. The switching function (δ) determines which half of the synergy the

input (EE) is mapped to. This is decided by the position of the reference joint ( ). This scaled single input (E) is then sent to the synergy controller. A, b,

and are vectors of joint amplitudes, position offsets, and phase offsets, respectively. These are used to scale each joint individually from one input. ε is

used to change the slope of the sliding manifold.

The control architecture for the Shadow Hand finger joint

synergy controller (Fig. 4) enables all of the DOFs involved

in the synergy to be controlled by a single input, E, which is

defined to be

EE is the amplified EMG signal measured from the

extensor digitorum communis (EDC) muscles of the human

test subjects, and saturates at 1 according to (8). The

switching term, δ, is defined to be 0 or 1 based on whether

or not the synergy is in the contact stroke or return stroke

portion of the synergy (Fig. 3(b)). This is determined by the

measured position of a reference joint ( . This joint is

given an initial phase offset of rad, with the remaining

joints being scaled in phase relative to this joint [8]. With

this offset, the joint position increases/decreases through the

first/second half of the synergy. Because the sine wave

parameters are known prior to implementation, the

maximum and minimum joint positions are known, and

these correspond to the endpoints of the fingertip trajectories

in Cartesian space. The value of δ is initially zero, causing a

contraction of the EDC to drive the fingers of the Shadow

Hand along the contact stroke of the synergy. When the

input E reaches , the reference joint (x2) is at its maximum

position. Once this condition is reached, the value of δ

switches to 1, and the signal is mapped to the return stroke

of the synergy as the muscle is relaxed. As E reaches zero, x2

reaches its minimum point, and the value of δ switches back

to zero. To facilitate proper behavior of the Synergy

Controller, relays are employed near the δ switching points.

This ensures that the controller switches properly in the

presence of noise or if E does not exactly reach 0 or 1.

The experimental setup of the Shadow Hand, explained

further in Section VII, is shown in Fig. 5(a). The

manipulator was securely mounted in the shown orientation,

while a single turn potentiometer attached to a cylinder acted

as the object that human operators used the Shadow Hand to

unscrew. Example data of the Shadow Hand performing the

unscrewing task under the EMG Synergy Controller are

shown in Fig. 6.

B. PID Sliding Mode Synergy Controller

Sliding mode control is a nonlinear technique that is often

used to robustly control nonlinear systems like the Shadow

Hand [9]. The benefit is that excellent error minimization

attributes are guaranteed within certain bounds even though

nonlinear disturbances are applied. This is a particularly

useful trait for this application to track the desired sinusoidal

angular position trajectories as the intermittent contact with

the object to be screwed or unscrewed will apply torques to

the motors involved in the synergy. To facilitate sliding

mode control, an error state is defined as

.

The sliding manifold for the system is written as

Fig. 6. Sample data from the Synergy Controller performing the

unscrewing task. The top graph illustrates the mapping of the amplified and

filtered EMG signal to the controller input . The middle graph

depicts the change in potentiometer position (α) and velocity as the Shadow

Hand is executing the contact stroke.

(a) (b)

Fig. 5. (a) Experimental setup of the Shadow Hand driven by the Synergy

Controller. The cap rotates about the axis α and has a range of motion of

6rad. (b) Experimental setup of the Motion Control Hand.

(b) Experimental setup of the Motion Control Hand.

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KI , KP , and KD are the diagonal

integral, proportional and derivative gain matrices,

respectively. The KI, KP, and KD gain terms are chosen so

that the poles of the system are in the left hand plane.

The PID sliding mode control law is then written as

,

where is the voltage input vector to the motors

(Fig. 4). is a diagonal matrix that is chosen as an

upper bound estimate on the motor voltages required to

overcome the torques applied to the joints of the Shadow

Hand that are involved in the synergies. The sat term is the

vector saturation function such that

T.

The sat function is used instead of the signum function

because it is piecewise continuous. The sat function partially

linearizes the control law to alleviate the chattering

phenomenon that is common with mechanical systems that

use a fully nonlinear control law associated with the signum

function. Incorporation of the integral error state ensures

zero steady state error.

VI. MOTION CONTROL HAND

A. Mechanism

The Motion Control Hand has a single DOF. Differential

equations to describe the system are given by

In these equations, represents the angular position and

is the angular velocity. Z, d, and I are the stiffness,

damping and inertia of the system, respectively. Z is

negligible before an object is grasped and xC is the angular

position when contact is initially established with the

environment. VM is the voltage input to the motor; N is a

factor based on the gear ratio, armature resistance, and

torque constant of the motor. D is the sum of unknown

internal and external disturbances applied to the hand which

may be nonlinear in nature.

The Motion Control Hand used in this paper is equipped

with an A1321 Hall effect sensor to measure the position

( ). Strain gauges mounted on the thumb measure the

normal force (FN).

B. Motion Control Hand Sliding Mode Controller

The sliding mode controller for the Motion Control Hand

has been described elsewhere [10] and is of the form

where VM is the voltage control law, R is a positive constant

and S1 is the sliding manifold comprised of position, velocity

and force feedback. The sliding mode controller is used to

improve the control of force and velocity for the prosthesis.

See [10] for a stability and robustness analysis of hybrid

force-velocity sliding mode control for this particular

system.

The sliding mode controller for the Motion Control Hand

requires two EMG inputs, placed on the EDC (EE) and flexor

carpi radialis (FCR) muscles of the forearm (EF). The two

signals are differenced to create a dual polarity signal which

is used to control the prosthesis. The fingertips from a

commercially available cosmetic glove are placed on the

Motion Control Hand to replicate frictional characteristics

that would be encountered during daily use by amputees

(Fig. 5(b)). Example data from a human operator using the

Motion Control Hand to perform the unscrewing task are

shown in Fig. 7. The difference in the measured EMG

signals (EE and EF) of the left arm is used to open or close

the gripper by alternating the relative contraction strength of

each muscle (Fig. 7, top). When the Motion Control Hand

has the potentiometer in grasp FN increases and the

prosthesis is manually turned with the right hand to induce a

change in the potentiometer angle α (Fig. 5(b)).

VII. EXPERIMENTAL METHODS: ARTIFICIAL HAND

CONTROL

Ten human test subjects gave informed consent prior to

participation in the experiments in accordance with IRB

protocol. An experimental apparatus consisting of a single

turn potentiometer attached to a 40mm diameter cylinder

was constructed to record the cylinder angle with respect to

time. Initially, each subject was asked to unscrew the

potentiometer with their own hand 10 times, with the

additional instruction to complete the task as quickly as

possible.

After this procedure was completed, the participants

then performed the task ten times using both the Motion

Control Hand and the Shadow Hand with the Synergy

controller (Fig. 5). Two EMG preamplifiers were placed on

the EDC and FCR of the subjects’ forearms prior to testing.

EMG signals were rectified, filtered and amplified using

Myolab II (Motion Control, Inc.). These filtered EMG

Fig. 7. The Motion Control Hand under sliding mode control. A contraction

of the EDC (EE) causes the Motion Control Hand to open, while a

contraction of the FCR (EF) causes the Motion Control Hand to close.

α α

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signals were used to control the Motion Control Hand with

MATLAB/Simulink using the real time windows target

kernel. In the case of the Shadow Hand, the EMG signals

were output from Simulink to a 12-bit oversampled analog-

to-digital converter.

Half of all test subjects began with the Motion Control

Hand first (Group 1), and the other half performed the task

with the Shadow Hand first (Group 2). Each participant

performed the unscrewing task 10 times with each system,

with the time to complete the task being tabulated. A single

factor ANOVA test was performed between the controllers

to determine if a statistical improvement was offered by the

Synergy Controller in regard to the time to complete the

task.

VIII. EXPERIMENTAL RESULTS

After data collection, the average time to complete the task

from the ten trials per person using each system was

calculated (Fig. 8). These values represent the average time

each subject took to complete the task with each system. The

composite averages and standard deviations across all

subjects was also tabulated (TABLE II). As expected,

subjects using their own hands completed the task in the

shortest time, with an average of 0.99s. Results between the

Motion Control Hand and Shadow Hand were more mixed,

with six of the ten subjects having a lower average

completion time with the Synergy Controller. The task was

completed (on average) in 6.21s and 5.17s using the Motion

Control Hand and Shadow Hand, respectively. The average

of all ten test subjects shows that the Synergy Controller

offered a 16.7% improvement in task completion time. This

improvement was substantially larger for Group 1, who

completed the task 28.2% faster with the Synergy

Controller. Subjects in Group 2 showed a 7.4%

improvement with the Synergy Controller (TABLE II).

A single factor ANOVA test was performed on these

values to determine if there existed a statistical difference in

the performance of each controller. Both the Shadow Hand

Synergy Controller and the Motion Control Hand were

compared to the human data and each other. The completion

time of each trial for each subject was used for the ANOVA

test, resulting in 100 data points each for the Motion Control

Hand, the Shadow Hand and the human hand. As expected, a

statistical difference between each of the robotic systems

and the human hand exists with a very high level of

confidence (p < 0.001). Between the two robotic systems,

the ANOVA test yields a value of p = 0.014. This indicates a

statistical improvement in the time to complete the

unscrewing task with the Shadow Hand Synergy Controller

with a 95% confidence interval (p < 0.05).

Due to the inherent differences (i.e. mechanical) between

the two systems, the improvement in completion time may

not be attributed fully to the difference in control methods.

More importantly however, the Synergy Controller offers a

novel method of coordinating multiple joint motions via a

single EMG input. This concept can be extended to other

synergistic motions, with a large potential for creating

complex motions with a low number of inputs.

IX. CONCLUSION

From observation of human data, a set of sinusoidal

trajectories were developed to mimic the human motion of

unscrewing a bottle cap with the thumb and index finger.

These trajectories were then implemented on a dexterous

robotic hand in the form a robustly stable sliding mode

algorithm driven by a single EMG signal. This synergy

controller was then evaluated against a one DOF prosthetic

hand also under sliding mode control to complete a timed

task in the minimum required time. In addition to

completing the task in a shorter amount of time (on average),

the developed Shadow Hand synergy controller has the

additional benefit of a greatly reduced workspace required to

complete the unscrewing task. Also, the Synergy Controller

requires only a single EMG input, while the Motion Control

Hand requires two for this task.

REFERENCES

[1] A. Kochan, "Shadow delivers first hand," Industrial Robot, vol. 32,

pp. 15-16, 2005.

[2] C. Cipriani, et al., "The smarthand transradial prosthesis," J. of

NeuroEng. and Rehab., vol. 8, pp. 1-13, 2011.

[3] A. Muzumdar, Powered upper limb prostheses. Berlin: Springer, 2004.

[4] C. Connolly, "Prosthetic hands from touch bionics," Industrial Robot,

vol. 35, pp. 290-293, 2008.

[5] H. Sears and J. Shaperman, "Proportional myoelectric hand control: an

evaluation," Am. J. of Phys. Med. and Rehab., vol. 70, pp. 20-28,

1991.

[6] F. Tenore, et al., "Towards the Control of Individual Fingers of a

Prosthetic Hand Using Surface EMG Signals," in Eng. in Med. and

Bio. Society, 2007. EMBS 2007. pp. 6145-6148.

[7] N. Karnati, et al., "Backdrivable periodic finger joint synergies:

human observations applied to a dexterous robotic hand," 2011 IEEE

RoBio Conf., Phuket Island, Thailand, 2011.

[8] N. Karnati, et al., "Human finger joint synergies for a constrained task

applied to a dexterous anthropomorphic hand," 2011 IEEE Humanoids

Conf., Bled, Slovenia, 2011.

[9] J. Slotine and W. Li, Applied nonlinear control. Upper Saddle River:

Prentice Hall, 2002.

[10] E. Engeberg, et al., "Hybrid force-velocity sliding mode control of a

prosthetic hand," IEEE Trans. on Biomed. Eng., vol. 55, pp. 1572-

1581, 2008.

Fig. 8. Averaged results of ten trials per test subject from each of the three

timed unscrewing tasks. Test subjects 1-5 began with the Motion Control

Hand first (Group 1). Test subjects 6-10 began with the Shadow Hand with

the Synergy Controller first.

TABLE II

TASK COMPLETION TIME (SECONDS)

System Group 1 Group 2 Average

Human Hand 1.04 ± 0.64 0.93 ± 0.30 0.99 ± 0.48

Motion Control

Hand 5.57 ± 1.83 6.85 ± 3.31 6.21 ± 2.61

Shadow Hand

Synergy Controller 3.99 ± 0.56 6.34 ± 2.23 5.17 ± 1.92

92