Model Adaptation with Least-Squares SVM
for Adaptive Hand Prosthetics
Francesco Orabona, Claudio Castellini, Barbara Caputo, Angelo Emanuele Fiorilla and Giulio Sandini
Abstract— The state-of-the-art in control of hand prosthetics
is far from optimal. The main control interface is represented
by surface electromyography (EMG): the activation potentialsof the remnants of large muscles of the stump are used in a nonnatural way to control one or, at best, two degrees-of-freedom.
This has two drawbacks: first, the dexterity of the prosthesis
is limited, leading to poor interaction with the environment;
second, the patient undergoes a long training time. As more
dexterous hand prostheses are put on the market, the need fora finer and more natural control arises. Machine learning canbe employed to this end. A desired feature is that of providing
a pre-trained model to the patient, so that a quicker and betterinteraction can be obtained.
To this end we propose model adaptation with least-squares
SVMs, a technique that allows the automatic tuning of thedegree of adaptation. We test the effectiveness of the approachon a database of EMG signals gathered from human subjects.
We show that, when pre-trained models are used, the numberof training samples needed to reach a certain performance isreduced, and the overall performance is increased, compared
to what would be achieved by starting from scratch.

I. I NTRODUCTION
In the framework of advanced, active hand prosthetics
we are witnessing a technology transfer from robotics andmechatronics. Touch Bionics’s i-LIMB [1] prosthetic hand,with its five degrees-of-freedom, is a real breakthroughwith respect to the previous state-of-the-art, Otto Bock’s
SensorHand Speed [2], which is essentially an open-closemechanism. Dexterity of hand prostheses is still far fromthat of state-of-the-art non-prosthetic mechanical hands, suchas, e.g., the DLR II [3] (not to mention a human hand, ofcourse), but things are getting better thanks to the aforementioned inter-disciplinary exchange (see Figure 1). Several
EU-funded projects (e.g., CyberHand [4] and SmartHand [5])testify the enthusiasm in the field.
The simplest, cheapest and therefore most used techniquefor interfacing the patient with the prosthesis is surface
electromyography (EMG): activation potentials of the patient’s stump residual muscles are detected to move the handto predefined positions. But the control schema employedis rather poor, using two or three electrodes to issue an“open/close” command or, in the more advanced case of thei-LIMB, to choose among a predefined set of grasp shapes.
F. Orabona (corresponding author) and B. Caputo are with Idiap Research Institute, rue Marconi, 19, Case Postale 592, CH-1920 Martigny,
Switzerland. e-mail: francesco.orabona@idiap.ch, barbara.caputo@idiap.ch
C. Castellini is with the LIRA-Lab, University of Genova, viale F. Causa,
13, 16145 Genova, Italy. e-mail: claudio.castellini@unige.it
A.E. Fiorilla and G. Sandini are with the DIST, University of Genova,
viale F. Causa, 13, 16145 Genova, Italy. and the Italian Institute of Technology, via Morego, 30, 16163 Genova, Italy. e-mail: emanuele.fiorilla@iit.it,
giulio.sandini@iit.it

(a)

(b)

(c)

Fig. 1. (a) Touch Bionics’s i-LIMB prosthetic hand (reproduced from[1]); (b) the DLR-II mechanical hand; (c) Otto Bock’s SensorHand Speed(reproduced from [2]).

In order to control the prosthesis in a more natural way,machine learning can be used to better interpret the standard
EMG signals. In the typical case, the patient is asked toimagine, e.g., a pinch grip; the related EMG pattern is thenused to obtain a pinch grip with the required force fromthe prosthesis. A degree of control unknown so far can bethus obtained, improving the patient’s life and shortening thetraining time. We envision adaptive prosthetics, a framework
where a patient enters a virtuous loop of reciprocal learning,whereas so far (s)he has to learn how to control the prosthesis
from scratch.
To further improve this loop it would be desirable to pretrain the prosthesis with a model which will be then refinedand adapted on-line to the patient. In machine learning, thisis called model adaptation: a system that adapts to a data
distribution as it shifts over time. In our proposed methodmodel adaptation works by constraining at each step a newmodel to be close to one of a set of pre-trained models stored
in the memory of the prosthesis. The degree of closenessand the choice of the pre-trained model to use are done
automatically by estimating the generalization power, using
the leave-one-out error.
To check whether this idea works we apply it to a set of
EMG data collected from 10 healthy subjects. Each subject
was asked to grasp a force sensor using three different grips;
meanwhile, we recorded the electrical activity of the muscles
that are most involved in the hand/wrist movements and theforce exerted by the subject on an off-the-shelf force sensor.
The experimental results show that our intuition is correct:
the proposed adaptation method, on average, shortens thetraining time and also gives better overall performances, withless training samples. This is true for the classification of thegrasp types and for the prediction of the force applied.

The paper is structured as follows: after a brief review ofrelated work, we describe our method (Section II) and the
EMG database (Section III). Section IV shows the experimental results and lastly Section V contains the conclusions.
A. Related WorkThe use of surface forearm EMG to control active handprostheses dates back to the Fifties and was brought tothe market by Otto Bock Orthopaedic Industry, Inc. [6].
EMG works by detecting a muscle’s activation potential,a fast oscillating signal whose root-mean-square is nonlinearly related to the force exerted by the muscle [7]. Since
amputees are usually left with little of their forearm, it has so
far been necessary to carefully detect the patient’s residual
muscles with the strongest activity. These muscles are used,
still nowadays, to control one, or at best two degrees-offreedom. For example, the usual control schema of Otto
Bock’s SensorHand Speed prosthetic hand maps wrist flexion
to hand closing and wrist extension to hand opening.
The situation hasn’t changed for a long time because
the EMG signal is badly conditioned, being influenced
by sweat, muscular fatigue, inter-arm differences and nonhand-related muscular activity (supination/pronation, walking, raising one’s arms and so on; see [8] for a survey).
Only in the 1990s it became apparent that machine learning
could be used to classify hand postures via the EMG. In theirseminal work, Bitzer and van der Smagt [9] used a Support
Vector Machine (SVM) to robustly classify six different handpostures. Neural networks and LWPR [10] have been used
to the same end (see, e.g., [11], [12], [13]) to classify up to
11 hand/finger postures and movements, and to approximate
the force involved in the grasp. As long as it is trained fora sufficient time, that is it explores a relevant portion of the
input space, a well-employed machine learning method will
be able to take into account all of the EMG signal’s problems.
As far as we know, there is no EMG database present
in the machine learning community, which could serve our
purpose. In some of the aforementioned papers, analogous
data sets (but most likely smaller than ours) are reported
about, but there is no mention of their availability. Regarding
model adaptation, several approaches has been proposed. In[14] many of them are compared and benchmarked, however
most of them are computationally inefficient because a retraining over new and old data is needed. An approach that
does not use re-training, based on SVM has been proposedin [15]. As far as we know this work is the first attempt touse model adaptation in the domain of EMG prosthetics.
II. M ATHEMATICAL F RAMEWORKThis section describes our mathematical framework. We
first introduce the basic notation (Section II-A), then we
present our algorithm for online model adaptation (SectionII-B).
A. Background
Assume xi ∈ Rm is an input vector and yi ∈ R is its
associated output. Given a set {xi , yi }li=1 of samples drawn

from an unknown probability distribution, we want to find afunction f (x) such that it determines best the corresponding
y for any future sample x. This is a general frameworkthat includes both regression and classification. The problem
can be solved in various ways. Here we will use kernel
methods and in particular Least-Squares Support Vector
Machines (LS-SVM) [16]. In LS-SVM the function f (x)is built as a linear model w · φ(x) + b, where φ(·) is a nonlinear function mapping input samples to a high-dimensional(possibly infinite-dimensional) Hilbert space called feature
space. Rather than being directly specified, the feature space
is usually induced by a kernel function K(x, x0 ) which
evaluates the inner product of two samples in the feature
space itself, i.e. K(x, x0 ) = φ(x) · φ(x0 ). A common kernel
function is the Gaussian kernel
K(x, x0 ) = exp(−γ||x − x0 ||2 )(1)that will be used in all our experiments.
The parameters of the linear model, w and b, are foundby minimizing a regularized least-squares loss function [16].
This approach is similar to the well-known formulation of
Support Vector Machines (SVMs), the difference being thatthe loss function is the square loss. While this does not induce a sparse solution, it makes it possible to write the leaveone-out error in closed form and with a negligible additional
computational cost [17]. This is known to be approximately
an unbiased estimator of the classifier generalization error[18]. This property is useful to find the best parameters forlearning (e.g. γ in (1)) and it will be used in our adaptation
method. Note that we use the same formulation to solve both
regression and classification problems.
B. Model Adaptation
Let us assume we have N pre-trained models stored inmemory, trained off-line on data acquired on N different
subjects. When the prosthetic hand starts to be used bysubject N + 1, the system begins to acquire new data. Given
the differences among the subjects’ arms and as well in theplacement of the electrodes, these new data will belong to a
new probability distribution, in general different from the N
previously modeled and stored. Still, as all subjects performthe same grasp types, it is reasonable to expect that the new
distribution will be close to at least one of those alreadymodeled; then, it should be possible to use one of the pretrained model as a starting point for training using the newdata. We expect that, by doing so, learning should be fasterthan using the new data alone. To solve this problem wegeneralize the framework for adaptation proposed in [15] for
SVMs: the basic idea is to slightly change the regularization
term of the SVM cost functional, so that the solution will be”close” to the pre-trained one. The optimization problem is
[15]
l
X
1
ξi
min kw − w0 k2 + C
w,b 2
i=1

subject to ξi ≥ 0, yi w · φ(xi ) + b ≥ 1 − ξi(2)where w0 is a pre-trained model. In order to tune thecloseness of w to w0 , we introduce a scaling factor βweighing the pre-trained model; also, we use the squareloss and therefore resort to the LS-SVM formulation. Thisway the leave-one-out error can be evaluated in closed form,
enabling automatic tuning of β. The optimization problem
reads now like this:
l
CX 2
1
ξ
min kw − βw0 k2 +w,b 2
2 i=1 isubject to yi = w · φ(xi ) + b + ξi

(3)and its solution is
w = βw0 +lX

αi φ(xi ), αi ∈ R .

(4)

i=1

Hence, the adapted model is given by the sum of the
pre-trained model w0 (weighted by β) and a new modelw obtained from the new samples. (Note that when β is
0 we recover the original LS-SVM formulation without any
adaptation to previous data.) As far as the leave-one-out error
is concerned, we have thatα = R(Y − β Ŷ )(5)

where α is the vector of the αi ’s in (4), Y is the vector of theyi , Ŷ is the vector of the predictions of the previous model,
and R = (K + 1/C)−1 with K denoting the kernel matrix,
i.e., Kij = K(xi , xj ). Let α0 = RY and α00 = RŶ ; fromthe equation above, and using the same steps in [19], wehave that the prediction on sample i, when removed fromthe training set, is
α00
αi0
+β i
(6)
Rii
Rii
from which the leave-one-out-error is easily evaluated, according to the required measure of accuracy for the problem
at hand. Notice that, in the above formula, β is the only
parameter; hence, it is possible to set it optimally in orderto minimize the leave-one-out error while at the same timechoosing the best pre-trained model for adaptation.
Notice also that the complexity of the algorithm is dominated by the evaluation of the matrix R, which must anyway
occur while training; thus, the computational complexity ofevaluating the leave-one-out error is negligible, if comparedto the complexity of training. As a last remark, we underlinethat the pre-trained model w0 can be obtained by any training
algorithm, as far as it can be expressed as a weighted sum of
kernel functions. The framework is therefore very general.
yi −III. DATABASE
A. Subjects and setup
We acquired data from ten healthy subjects, two womenand eight men, nine right-handed and one left-handed, ofan average age of 30.9 ± 8.45 years. The subjects weregenerally naı̈ve with respect to the recording procedure. We
placed on each subject’s dominant forearm 7 surface EMG
electrodes. The number of electrodes and their positions werechosen, visually and by palpation, according to the medical
literature [20]. This procedure allowed us to identify the most
relevant flexor and extensor muscles of the forearm, and torecord their EMG activity from the spots that should be least
affected by signal cross-talk1 . The chosen locations were:• on the forearm ventral side: near the wrist, above
the flexor pollicis longus; centrally, above the flexor
digitorum superficialis; near the elbow, above the flexor
digitorum profundus; and near the wrist, above the flexor
digitorum superficialis again;• on the forearm dorsal side: near the wrist, above the extensor pollicis brevis/abductor pollicis longus; centrally,
above the extensor digitorum communis and extensor
digiti minimi.
We employed the electrodes Aurion ZeroWire wireless
EMG electrodes [21]. Moreover the subjects were given a
FUTEK LMD500 Hand Gripper force sensor [22] in order
to measure the force applied by her/his hand during therecording.
We used a standard National Instruments data acquisitionboard (NI-USB6211) connected to the receiver of the EMG
wireless device and to the force sensor, in order to record thesensors’ signals and the exerted force. We set the sampling
rate of the board at 2kHz, since it is known that the raw
EMG relevant bandwidth lies between 15 and 500Hz. See
Figure 3 for an example.
B. Data acquisition and pre-processing
We first considered a rest condition, so to define thebaseline of the EMG activity. We then proceded with thedata recording: the subject kept her/his arm still and relaxedon a table, and was asked to grasp the force sensor using, inturn, three different grips (Figure 2).
The subject freely repeated each grasping action for 100
seconds, resting for 30 seconds in between grasps. In orderto gather more data and diminish the effect of local errors,
the whole procedure was repeated twice. As a whole, eachsubject’s recording resulted in about 2.4 × 106 samples.
Unlike commercial EMG electrodes, such as, e.g., Otto
Bock’s MyoBock electrodes [23] that return the on-board
computed Root-Mean Square (RMS) of the EMG signal,
the electrodes employed here return the “raw” EMG signal.
Nevertheless, it is well-known [7], [8] that the force exerted
by a muscle is strongly related to the RMS of the EMG
signal, rather than to the raw signal. For this reason, inorder to have a signal that is as similar as possible to acontrol signal, we decided to evaluate the RMS, electrodeby electrode.
For a given mono-variate discrete time-varying signal, theRMS is defined as the mean of the squares of the signal values, evaluated over a certain time-window TRM S . Roughly
1 But notice that some of the aforementioned muscles are deep into theforearm, so that muscle cross-talk cannot be completely avoided.

(a)Fig. 2.

(b)(c)

The three different grips employed in the experiment: (a) index precision grip; (b) other fingers precision grip; (c) power grasp.

(a)Fig. 3.

(b)(a) typical raw EMG and force signals; (b) frequency diagram of the EMG signal.

speaking, the RMS acts like an envelope extraction plus a
low-pass filter, whose cutoff frequency grows smaller as thetime-window grows larger (i.e., as TRM S becomes higher).
For this reason, high values of TRM S imply an ostensible
delay in the resulting signal that is due to responsiveness ofthe synthesized output signal. It becomes slower and slower
as the TRM S value increases, since more “samples” areaveraged to obtain a significant value. The choice of TRM S
is therefore crucial to produce a signal which is maximally
related to the force signal, unaffected by high-frequency
noise, and with an acceptable lag. However, it must be
noted here that the EMG signal, being directly related tothe muscle activation potentials, happens to anticipate themuscle movements2 . Therefore, in practical applications, it
can be considered as acceptable a wider lag than what one
would expect. This is useful since it allows us to increase
TRM S , if necessary.
We are not aware of any systematic way of setting a goodvalue of TRM S in such a framework. Therefore we found
TRM S heuristically, according to some initial experiments.
Figure 3 (panel (a)) shows a few seconds of typicalforce/EMG behavior: it is apparent that the EMG signal starts
2 The electromechanical delay (EMD) of a muscle is defined as the interval
between the onset of the electrical activity of the muscle (EMG) indicating
its activation by the neural system and the onset of the resulting change
in the mechanical variable observed. The delays reported range from 25 to
100ms for different muscles and tasks [24].

oscillating when the force signal starts increasing. It is alsoquite clear that the amplitude of the envelope of the EMG isrelated to the force, as indicated in the literature. Panel (b)shows the frequency analysis of the same EMG signal: as
one can see, the meaningful bandwidth lies in the interval
known from the literature.
This enables us to safely sub-sample the EMG signal
after having applied the RMS. Assuming that TRM S is
not too small, we subsampled both the EMG and force
signal at 25Hz, taking one sample every 80 of the original
sequence. This considerably reduced the amount of data to beprocessed, namely to about 30.000 samples for each subject.
As a last data pre-processing step, we removed from thesample set those samples for which the applied force waslower than a specific threshold, in order to get a clearer
representation of the activation potentials. This threshold waschosen in order to remove a minimal fraction of the samples.
Of course, we fully retained the samples corresponding to thebaseline rest condition. This is why we chose to record thiscondition before the data acquisition.
IV. E XPERIMENTAL R ESULTS
We conducted two different experiments, one to predict
the force measured by the force sensor and another one toclassify the grasp type.
As already mentioned in Section II-B, our working assumption is to have N pre-trained models stored in memory;new data comes from subject N + 1 and the system starts
training, to build the N + 1’th model. The performance isthen evaluated using unseen data from subject N + 1. To
simulate this scenario and to have a reliable estimation of
the performance, we use a leave-one-out approach: out of
the 10 subjects for which we have data recordings, we train
9 models off-line. These correspond to the N stored models
in memory, while data from the remaining subject are used
for the adaptive learning of the N + 1’th model. The training
sequences are random subsets from the entire dataset, that isthey are taken without considering the order in which they
were acquired. This procedure is repeated 10 times, using inturn all the recorded subjects for the adaptive learning of themodel.
To assess the performance of the proposed adaptation
method we compared it to two baseline methods. The firstone, that we call Prior, consists in using only the pre-trainedmodels without updating them with the new training data.
Therefore we consider only the best performance obtainedby one of the 9 pre-trained models, corresponding to thebest-case scenario. The second one, NoAdapt, is plain LSSVM using only the new data for training, as it would be in
the standard scenario without adaption.
As a measure of performance, for classification we use thestandard classification rate; for regression, the performanceindex is the correlation coefficient evaluated between thepredicted force signal and the real one. The choice of the correlation coefficient, as opposed to the more standard MeanSquare Error, is suggested by a practical consideration: whendriving a prosthesis, or even a non-prosthetic mechanicalhand, we are not interested in the absolute force values
desired by the user/subject, since mechanical hands usuallycannot apply as much force as human hands do, for obvious
safety reasons3 . We are rather concerned about getting asignal which is strongly correlated with the user/subject’s
will.
To build the pre-trained models we used the standard SVM
algorithm. All the parameters to be set during training (C andγ of the gaussian kernel) were chosen by cross-validation.
In the following Figures, error bars (when present) denote±1 standard deviations with respect to the average values.
We have considered a maximum of 360 training samples
and the final performance, averaged across the subjects, for
NoAdapt is 89.2% and 0.811, respectively for classification and regression. With our adaptation method we getinstead 90.2% and 0.85. Figure 4 shows in more details
the difference in classification (panel (a)) and regression(panel (b)) performance obtained by our method with respect
to NoAdapt. As one can see, adaptation uniformly obtains
a better performance, with the exception of classification
when the number of samples is below 150: in that casea slight loss of about 1% can appear. In the worst cases,
the performance of NoAdapt is re-obtained. Notice also thatstandard deviations are rather large when training is done
3 Or, e.g., in teleoperation scenarios, they could be able to apply much
more force than a human hand can.

on too few samples. This is due to the high variance of theleave-one-out error technique when too few training samples
are considered.
Depending on the subject, the improvement can be quite
large: up to about 15% higher rate for classification andabout 0.15 points stronger correlation for regression. In
average, for classification, the gain is almost 5% when there
are only 30 training samples. It settles to around 1% withsmaller standard deviation, as the number of training samples
increases. For regression, we have a correlation coefficient
about 0.04 points uniformly stronger in average.
To get a more detailed idea of the results obtained, consider now Figure 5, concerning the classification experiment.
Panel (a) shows the performance obtained on the best-casesubject, that is, a subject for whom a very good match has
been found among the pre-trained models, while panel (b)shows the performance for the worst-case subject. In thebest case the gain is about 3% after 360 samples, whilein the worst case we basically re-obtain the performanceof NoAdapt, as soon as enough samples from the new
distribution are considered. Essentially, our method improves
things if a good match can be found, and does no harm
if none exists. Similar observations can be done for the
regression task (Figure 6). In the best case, the correlation
is about 0.06 points uniformly stronger, whereas in theworst case NoAdapt’s performance is obtained. Note also theperformance of Prior, constantly inferior to NoAdapt and our
method.
The worst-case subjects represent the paradigmatic caseof no previous models matching the current distribution; asa consequence, the parameter β was automatically set to avery small value. In this case, there is essentially no transfer
of prior knowledge. But it is reasonable to claim that theoverall performance of the method would increase along with
the number of stored models, since this would mean a larger
probability of finding a matching pre-trained model.
In the long run, a large database of pre-trained models,possibly categorized in order to avoid too hard a computational burden, would definitely help getting uniformly better
performances.
V. C ONCLUSIONS
The model adaptation method presented in this paper
stems from a problem in adaptive hand prosthetics, namely:is it possible to help a patient to learn to use a dexterous
hand prosthesis by exploiting the common features found inmodels trained upon other patients? The answer, at least as
far as healthy subjects are concerned, is yes: we have hereby
presented a novel method for model adaptation in machinelearning, using Least-Squares SVMs; the idea is to build a
SVM solution which is close to one of a set of pre-storedmodels. The choice of which model to use among the pretrained ones, as well as the parameter β, determining thedegree of closeness to start the training from, are completely
automatic, as we use an estimation of the generalization error.
We tested our method on a database built with EMG andforce data from 10 healthy subjects, trying to improve the(a)Fig. 4.

(b)Classification (a) and regression (b) performance difference obtained by our method with respect to NoAdapt.

(a)

(b)

Fig. 5. Classification: (a) classification rate gain of the adapted model compared to NoAdapt and Prior on the best-case subject; (b) classification rate
gain for the worst-case subject.

(a)

(b)

Fig. 6. Regression: (a) correlation strength gain of the adapted model compared to NoAdapt and Prior for the best-case subject; (b) correlation gain for
the worst-case subject.

training times and asymptotic performance of one subject

by pre-training on other subjects. The outcome of the ex-periment is positive: our method gains consistently both in
the classification and regression tasks in the best and averagecases, and it resorts to the non-adaptive performance in theworst.
Therefore, it is apparent that a large amount of knowledge
stored in LS-SVM models is common to all subjects, which
is obviously due to the analogies among the tasks performedby the subjects, as well as to the anatomical similarities
among the arms and the careful positioning of the electrodeson the subjects’ forearms. A further interesting point is that,
almost uniformly, models obtained by adaptation from a pretrained model obtain a better performance than those trained
from scratch. This result is somehow surprising, althoughvery encouraging, and subject of future research.
Notice that we present no results on a real prosthetic/robotic hand so far — this is subject of immediatefuture research. We successfully applied a similar system
to the DLR-II mechanical hand (see [11], [12]), and since
the accuracy of the system presented here is analogous tothat of the one therein, there is no reason why the resultspresented here should not apply as well in the practicalcase. One interesting possibility is that of using this system
to speed up the adaptation of an already existing dexterous
hand prostheses, such as, e.g., Touch Bionics’s i-LIMB [1]prosthetic hand, as already mentioned in the introduction.
Lastly, let us consider the fact that, most likely, the overall
performance of the method will increase when more subjects
are available, since this would mean a larger probabilityof finding a matching pre-trained model. In a clinical setting, this means that after an experimental phase, adaptive
prostheses employing this method could actually be built.
It remains, of course, to discover whether this idea can betransferred to amputees: amputations are, obviously, noncontrolled, traumatic events (except in some cases), andtherefore stumps exhibit much more variability than healthy
forearms. This is the subject of ongoing as well as future
research.
ACKNOWLEDGMENTS
This work is partially supported by the project NEURObotics, FP6-IST-001917 (CC) and the project DIRAC,
FP6-IST-027787 (FO,BC). We thank Thierry Pozzo andMarco Jacono for their support during the experiment andElisa Ricci for the useful discussions on the paper.
R EFERENCES[1] “The i-LIMB hand prosthesis,” 2007. [Online]. Available: http://www.touchbionics.com[2] “The Otto Bock SensorHand hand prosthesis,” 2008.
[Online]. Available: http://www.ottobockus.com/products/upper limb
prosthetics/myoelectric hands sensorhand.asp[3] H. Huang, L. Jiang, D. Zhao, J. Zhao, H. Cai, H. Liu, P. Meusel,
B. Willberg, and G. Hirzinger, “The development on a new biomechatronic prosthetic hand based on under-actuated mechanism,” in Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent
Robots and Systems, 2006, pp. 3791–3796.
[4] “The CyberHand project,” 2007. [Online]. Available: http://www.
cyberhand.org[5] “The SmartHand project,” 2007. [Online]. Available: http://www.
elmat.lth.se/∼smarthand[6] J. A. B. Wilson, “History of amputation surgery and prosthetics,” in Atlas of Limb Prosthetics: Surgical, Prosthetic, and Rehabilitation Principles, 2nd ed., H. K. Bowler and J. W. Michael, Eds. C. V. Mosby,
1981, pp. 3—23, available at http://www.oandplibrary.org/alp/chap0101.asp.
[7] C. J. De Luca, “The use of surface electromyography inbiomechanics,” 1997. [Online]. Available: http://www.health.uottawa.
ca/biomech/courses/apa4311/biomec∼1.htm[8] M. Zecca, S. Micera, M. C. Carrozza, and P. Dario, “Control ofmultifunctional prosthetic hands by processing the electromyographic
signal,” Critical Reviews in Biomedical Engineering, vol. 30, no. 4–6,
pp. 459–485, 2002.
[9] S. Bitzer and P. van der Smagt, “Learning EMG control of a robotic
hand: Towards active prostheses,” in Proceedings of ICRA, International Conference on Robotics and Automation, Orlando, Florida,
USA, may 2006, pp. 2819–2823.
[10] S. Vijayakumar, A. D’Souza, and S. Schaal, “Incremental online
learning in high dimensions,” Neural Computation, vol. 17, pp. 2602–
2634, 2005.
[11] C. Castellini, P. van der Smagt, G. Sandini, and G. Hirzinger, “Surface
EMG for force control of mechanical hands,” in Proceedings of ICRA08 - International Conference on Robotics and Automation, 2008, toappear.
[12] C. Castellini and P. van der Smagt, “Surface EMG in advanced handprosthetics,” Biological Cybernetics, 2008, to appear.
[13] F. C. P. Sebelius, B. N. Rosen, and G. N. Lundborg, “Refined
myoelectric control in below-elbow amputees using artificial neuralnetworks and a data glove,” J Hand Surg [Am], vol. 30, no. 4, pp.
780–789, Jul 2005.
[14] H. Daumé III, “Frustratingly easy domain adaptation,” in Conferenceof the Association for Computational Linguistics (ACL), Prague, Czech
Republic, 2007.
[15] J. Yang, R. Yan, and A. G. Hauptmann, “Adapting SVM classifiers
to data with shifted distributions,” in ICDMW ’07: Proceedings of theSeventh IEEE International Conference on Data Mining Workshops.
Washington, DC, USA: IEEE Computer Society, 2007, pp. 69–76.
[16] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector
Machines (and Other Kernel-Based Learning Methods). Cambridge
University Press, 2000.
[17] R. Rifkin and R. Lippert, “Notes on regularized least-squares,” Massachusetts Institute of Technology, Cambridge, MA, Tech. Rep. CBCL
Paper #268/AI Technical Report #2007-019, May 2007.
[18] A. Luntz and V. Brailovsky, “On estimation of characters obtainedin statistical procedure of recognition (in russian),” Techicheskaya
Kibernetica, vol. 3, 1969.
[19] G. C. Cawley, “Leave-one-out cross-validation based model selection
criteria for weighted LS-SVMs,” in In Proceedings of the International
Joint Conference on Neural Networks (IJCNN-2006), Vancouver, BC,
Canada, 2006, pp. 1661–1668.
[20] F. P. Kendall, E. K. McCreary, P. G. Provance, M. M. Rodgers, and
W. Romani, Muscles: Testing and Function, with Posture and Pain.
530 Walnut St.Philadelphia, PA 19106-3621: Lippincott Williams &Wilkins, 2005.
[21] “Aurion zerowire EMG electrodes.” [Online]. Available: http://www.aurion.it/[22] “Futek LMD500 medical load cell (hand).” [Online]. Available:
http://www.futek.com/product.aspx?stock=FSH00125[23] “Otto Bock MYOBOCK 13E200=50 surface emg electrodes,” 2008.
[Online]. Available: http://www.ottobockus.com/products/upper limb
prosthetics/myoelectric hands myobockr.asp[24] W. Wolf, C. Staude, and U. Appel, “Enhanced onset detection accuracy“reduces” the electromechanical delay of distal muscles,” in Proc. 16th
Annual International Conference of the IEEE Engineering Advances:
New Opportunities for Biomedical Engineers Engineering in Medicine
and Biology Society, 3–6 Nov. 1994, pp. 392–393.