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Abstract² Several efforts have been carried out in the past

to develop hand prostheses controllable by the voluntary

activities of amputees and able to restore lost hand functions.

Myoelectric prostheses represent a viable clinical solution

thanks to non invasiveness and recording easiness of

electromyographic signals (EMGs).

The control of multi-degree of freedom (DoFs) prostheses in

an effective and natural way is currently limited by the need of

a complex pattern recognition approach and the use of "non

homologous" muscles. Beside solutions based on pattern

recognition in the central nervous system, the use of electrodes

implanted into muscles or peripheral nerves or targeted muscle

reinnervation, a possible solution for the development of a more

"natural" EMG-based control strategy could be the

discrimination of grasping tasks during the reaching phase.

In this pilot study, experiments with three able-bodied

subjects have been carried out in order to verify whether this

strategy can be implemented. A support vector machine

algorithm has been used for the prediction of different grip

types during reach to grasp movements using EMG activity of

distal and proximal upper limb muscles. The information

coming from proximal muscles helped to increase robustness in

the classification tasks.

I. INTRODUCTION

lectromyographic signals recorded with surface

electrodes (sEMG) represent an easy and non-invasive

way to obtain information about muscle activation [1].

The electrical activity of skeletal muscles has been used in

basic science studies (e.g., neuroscience, motor control),

clinical applications, rehabilitation and sports medicine as

well as bioengineering. In particular, sEMG has been used in

the recent past for the control of exoskeletons, tele-operated

robots, and artificial prostheses (see [2-5] for some examples

of these systems).

Manuscript received January 31, 2012.

J. Carpaneto is with the BioRobotics Institute, Scuola Superiore

6DQW¶$QQD��3LVD��������,WDO\ (e-mail: j.carpaneto@sssup.it).

K.H. Somerlik is with INOMED GmbH, Emmendingen and the

Bernstein Center Freiburg, Albert-Ludwig-University, Freiburg, Germany

(e-mail: k.somerlik@inomed.com).

T.B. Krueger is with INOMED GmbH, Emmendingen, Germany (e-

mail: t.krueger@inomed.com).

T. Stieglitz is with the Laboratory for Biomedical Microtechnology,

Department of Microsystems Engineering and the Bernstein Center

Freiburg, Albert-Ludwig-University, Freiburg, Germany (e-mail:

Thomas.Stieglitz@imtek.uni-freiburg.de).

S. Micera is with the BioRobotics Institute, Scuola Superiore

6DQW¶$QQD�� 3LVD�� ������ ,WDO\ and with the Center for Neuroprosthetics,

Ecole Polytechnique Federale de Lausanne, Lausanne, 1015 Switzerland

(e-mail: micera@sssup.it).

Myoelectric prostheses represent a clinical solution for

upper limb amputations [6], less invasive than other

interesting approaches based on targeted muscle

reinnervation and electrodes implanted into muscles or

peripheral nerves that are currently under investigation [7-

10]. One of the main drawbacks of the EMG-based control

of hand prostheses is the need for coding the different

actions RI� WKH� SURVWKHVLV¶� FRQWURO� XQGHU� WKH� ODFN� RI�

³Komologous´ muscles (e.g., extension of the fingers of a

prosthetic hand must be coded using different muscular

activities such as the ones of upper arm or forearm). This

leads to a difficulty in the use of dexterous prostheses with

several DoFs that must be controlled only by means of

complex pattern recognition approaches [6] from a

computational and cognitive point of view.

The possibility to identify and discriminate grasp patterns

depending on the object to be grasped by looking exclusively

at the different activities of more proximal upper limb

muscles seems an interesting approach in order to implement

a more "natural" control of hand prostheses. The rationale

for this choice can be found in recent studies that

demonstrated object-dependent modulation of EMG activity

patterns during different grasping tasks in monkeys [11] and

in able-bodied subjects [12].

In this paper, a pattern recognition algorithm able to

discriminate EMG signals recorded from the distal and the

proximal muscles of able bodied subjects during reach to

grasp movements has been developed.

II. METHODS

A. Subjects

Three able-bodied subjects (1 male, 2 females, mean age

26.03 years, SD 0.74, all right-handed) provided their

consent to participate in this study after being informed

about the experimental procedures. All subjects were healthy

without any known neurological abnormalities or musculo-

skeletal disorder.

B. Experimental set-up

Subjects sat comfortably in front of a table with the

dominant arm in an assigned resting position (muscles

relaxed). They were ask to reach and grasp for 2 seconds six

objects placed in four different table positions (F, R, L, and

H, see Figure 1): a beaker (diameter: 6.6 cm) and a cylinder

(diameter: 4.8 cm) for the cylindrical grip, a Christmas bowl

(diameter: 3.8 cm) and a tennis ball (diameter: 6 cm) for the

Natural muscular recruitment during reaching tasks to control hand

prostheses

J. Carpaneto, K.H. Somerlik, T.B. Krueger, T. Stieglitz, Senior Member IEEE and, S. Micera, Senior

Member IEEE

E

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 165

tripod grip and finally a key (height: 0.5 cm) and a videotape

(height: 2 cm) for the lateral pinch. The movement started

from a position of the hand, flat on the table, in the midline

of the body at a distance of about 8 cm from the chest. The

objects were placed in order to allow the subjects to perform

the maximal elbow extension in three directions (position

L = 45o in the contralateral hemisphere, position F = 0

o,

position R = -45o in the lateral hemisphere). R, L, and F

positions lie in the table plane whereas position H is created

to consider an upwards directed movement thanks to an

adjustable platform. All the distances between start position

and objects were adjusted according to the anthropometric

data of every subject. Each subject grasped each object 10

times for every position at a free arm speed (in total 10x6x4

grasping trials) with a rest period to avoid muscle fatigue.

The order of the grasping trials was arranged randomly to

exclude short-term learning phenomena. Subjects were

requested not to bend and rotate the trunk and to prevent

translational movements at the shoulder. Moreover,

particular attention was devoted to verify the correct posture

of the subjects during sitting before starting the experiments.

Fig. 1. Experimental set-up: (left) the subjects were asked to reach and

grasp different objects placed in four different positions along the

specified lines (L, F(H), and R). The exact position was determined

according to the length of dominant upper limb. (right) Examples of

three objects with flexible conductor boards.

Raw sEMGs were collected by means of disposable

surface electrodes (50 mm diameter, pre-gelled Ag/AgCl,

Pirrone & Co, Milano, Italy) and a wireless commercial

system (TeleMyo 2400R and TeleMyo 2400T by Noraxon,

Scottsdale, AZ, USA). Raw data were acquired in bipolar

configuration (interelectrode distance: 10 mm) at a frequency

of 1.5 kHz, using 1st order 10 Hz hardware high pass filter,

8th

order 500 Hz hardware Butterworth low pass anti-aliases

filter, resolution of 12 bits, hardware gain of 500 and stored

for offline analysis in Matlab environment (The MathWorks,

Natick, MA, USA). An additional digital high pass filter

(6th

order 20 Hz Butterworth) was used in order to remove

residual offset.

Electrodes were placed over 14 muscles according to [13]:

(shoulder region) M. pectoralis major, M. latissimus dorsi

M. trapezius (pars descendens), M. deltoideus posterior (pars

spinalis), M. deltoideus medius (pars acromialis), M.

deltoideus anterior (pars clavicularis); (upper arm) M. triceps

brachii (caput laterale), M. triceps brachii (caput longum),

M. biceps brachii ); (forearm) M. extensor digitorum, M.

flexor carpi radialis, M. brachioradials, M. extensor carpi

ulnaris, M. flexor digitorum superficialis.

Flexible conductor boards (copper-plated polyimide) were

glued on the table, the objects, middle finger and thumb of

the subjects in order to obtain synchronization signals (i.e.,

start of the reaching movements and contact with the

objects). The response time of these boards was 2-4 �V�ZKLFK

was faster than required. These signals have been recorded

using 2 input channels of the wireless TeleMyo system.

C. Data analysis

EMGs during the reaching phase were stored together

with 1000 samples before the start of the movement and

1000 samples after reaching the object, according to the

information provided respectively by the sensors placed over

the table and over the objects. Even if several set of features

and classifiers have been proposed for the classifications of

sEMGs [6], the combined use of a single feature (i.e.,

integrated absolute value (IAV)) and the support vector

machines (SVMs) as classifier [14] is an easy and efficient

way to obtain results similar to the state of the art (i.e, use of

multifeature time domain feature and classifiers such as

neural networks and linear discriminant analysis (LDA) [5,

15, 16]). The IAV is defined as the sum of the absolute value

of sEMG over a predefined interval i from t1to t2.

¦

2

1

t

tk

ki sEMGIAV

The IAV was always extracted for each muscle and each

grip using fixed percentage windows: the time of the grasp

(or reaching time) from the start of the movement to the

contact with object was taken as 100% of the movement.

IAVs were calculated using window sizes set equal to 25%,

50%, 75% and 100% of this time. No features for the resting

position were computed. Therefore these features have only

been used to distinguish between different grips.

SVMs were used for the classification of the different

grips during the reaching phase. Data sets with patterns

belonging to different classes were built. In particular, C-

SVMs with radial basis function kernel were chosen (C and ��

selected after a grid optimization using four-fold cross-

validation) using LIBSVM toolbox [17]. The classifier was

trained with data recorded from 50% of the trails and tested

on the other half, selecting in a random way an equal number

of trials for each class. The performance of the classifier was

expressed with the recognition ratio (RR), the ratio between

the number of correctly identified grips and the size of the

test set.

Different (reduced) muscle sets have been selected in

order to simulate different amputation levels and to evaluate

the effects of the use of more proximal muscles on the

166

167

In Figure 5 are shown the results of the classification as a

function of different muscle sets. RRs similar to those

obtained with all the 14 muscles were obtained with subset

R1 (e.g., RRs bigger than 85% by means of IAVs calculated

from 75% of the movement phase; one subject was able to

reach a RR of 98.3%) whereas RRs significantly decreased

with subsets R2 and R3.

A R1 R2 R30

25

50

75

100

RR

(%

)

25%

A R1 R2 R30

25

50

75

100

RR

(%

)

50%

A R1 R2 R30

25

50

75

100

RR

(%

)

75%

A R1 R2 R30

25

50

75

100

RR

(%

)

100%

A R1 R2 R30

25

50

75

100

RR

(%

)

25-75%

muscle setsA R1 R2 R3

0

25

50

75

100

RR

(%

)

25-100%

muscle sets Fig. 5. Mean ± SD RRs as a function of the different muscle sets used.

A: all the 14 muscles; R1: all the 14 muscles except extensor and flexor

digitorum; R2: muscles located in the upper arm and shoulder region;

R3: the pectoralis major, the trapezius descendens, the latissimus dorsi

as well as the deltoid posterior and anterior

IV. DISCUSSION

In the last decades several pattern recognition algorithms

for the development of EMG-based control of hand

prostheses have been developed [4, 6]. Most of them were

based on a control scheme that was not intuitive and difficult

to be used especially for the control of multi DoFs

prostheses. Recently, some groups analyzed and tried to

characterize patterns of muscle activity during object

reaching and grasping [11, 12].

In this paper, SVM classification of the activity of

proximal and distal upper limb muscle sets in able-bodied

subjects has been done in order to investigate the

contribution of different muscle sets simulating diverse

amputation levels. Even if the use of windows that cover

different percentages of reaching movements could not be

appropriate for an on-line control, a characterization of the

activity of these muscles represents a preliminary step for the

development of new sEMG-based control strategies.

According to the results obtained, it seems possible to

discriminate between three different grips (i.e., cylindrical,

tripod, and lateral) with mean accuracy near 90% using

windows that cover respectively the 75% and the 100% of

the reaching phase and the IAV extracted from all the

muscles or from the subset R1 that simulates a transcarpal

amputation. sEMGs from upper arm and shoulder muscles

(subset R2) seem to contain enough information to classify

grip types with RRs ~70-80% whereas the use of shoulder

muscles only (subset R3) seems not sufficient for the

prediction of the intended grip.

Even if more extensive experiments need to be carried out

in order to obtain more clear results, the use of proximal

sEMGs seems an interesting supplementary information that

could be integrated in a prosthesis control scheme, together

for example with inertial sensors [18], in order to increase

the robustness and reduce the cognitive effort for amputees.

REFERENCES

[1] R. Merletti, et al., "Technology and instrumentation for detection

and conditioning of the surface electromyographic signal: state of the

art," Clin Biomech (Bristol, Avon), vol. 24, pp. 122-134, 2009.

[2] J. Rosen, et al., "A myosignal-based powered exoskeleton system,"

IEEE Trans Syst Man Cybern A, Syst Humans, vol. 31, pp. 210-

222, 2001.

[3] O. Fukuda, et al., "A human-assisting manipulator teleoperated by

EMG signals and arm motions," IEEE Trans Robot Autom, vol. 19,

pp. 210-222, 2003.

[4] K. Ohnishi, et al., "Neural machine interfaces for controlling

multifunctional powered upper-limb prostheses," Expert Rev Med

Devices, vol. 4, pp. 43-53, 2007.

[5] B. Hudgins, et al., "A new strategy for multifunction myoelectric

control," IEEE Trans Biomed Eng, vol. 40, pp. 82-94, 1993.

[6] S. Micera, et al., "Control of Hand Prostheses Using Peripheral

Information," IEEE Rev Biomed Eng, vol. 3, pp. 48-68, 2010.

[7] T. A. Kuiken, et al., "Targeted muscle reinnervation for real-time

myoelectric control of multifunction artificial arms," JAMA, vol.

301, pp. 619-628, 2009.

[8] S. Micera, et al., "Decoding Information From Neural Signals

Recorded Using Intraneural Electrodes: Toward the Development of

a Neurocontrolled Hand Prosthesis," Proc IEEE, vol. 98, pp. 407-

417, 2010.

[9] D. Farina, et al., "Multichannel thin-film electrode for intramuscular

electromyographic recordings," J Appl Physiol, vol. 104, pp. 821-

827, 2008.

[10] R. F. Weir, et al., "Implantable myoelectric sensors (IMESs) for

intramuscular electromyogram recording," IEEE Trans Biomed Eng,

vol. 56, pp. 159-171, 2009.

[11] T. Brochier, et al., "Patterns of muscle activity underlying object-

specific grasp by the macaque monkey," J Neurophysiol, vol. 92, pp.

1770-82, Sep 2004.

[12] C. Martelloni, et al., "Characterization of EMG patterns from

proximal arm muscles during object- and orientation-specific

grasps," IEEE Trans Biomed Eng, vol. 56, pp. 2529-36, Oct 2009.

[13] F. P. Kendall, Muscles; Testing and Function with Posture and Pain

Baltimore, Maryland, 2005.

[14] M. A. Oskoei and H. Hu, "Support vector machine-based

classification scheme for myoelectric control applied to upper limb,"

IEEE Trans Biomed Eng, vol. 55, pp. 1956-1965, 2008.

[15] K. Englehart and B. Hudgins, "A robust, real-time control scheme

for multifunction myoelectric control," IEEE Trans Biomed Eng, vol.

50, pp. 848-854, 2003.

[16] K. Englehart, et al., "Classification of the myoelectric signal using

time-frequency based representations," Med Eng Phys, vol. 21, pp.

431-438, 1999.

[17] C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector

machines," ACM Trans Intell Syst Technol, vol. 2, pp. 1-27, 2011.

[18] A. Fougner, et al., "Resolving the limb position effect in myoelectric

pattern recognition," IEEE Trans Neural Syst Rehabil Eng, vol. 19,

pp. 644-51, Dec 2011.

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