application of multimedia techniques in the physical rehabilitation of parkinson's patients

THE JOURNAL OF VISUALIZATION AND COMPUTER ANIMATION

J. Visual. Comput. Animat. 2003; 14: 269–278 (DOI: 10.1002/vis.324)* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

Application ofmultimedia techniquesin thephysical rehabilitationof Parkinson’s patients

By Antonio Camurri, BarbaraMazzarino,GualtieroVolpe*, PietroMorasso,Federica Priano andCristina Re* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

This paper presents and discusses some experiments having the purpose of planning,

developing and validating aesthetically resonant environments for different types of

sensorimotor impairments which affect Parkinson’s patients. From a technical point of view

the aim is to develop a computational open architecture in which it is possible to integrate

modules for gesture analysis and recognition and for interactive construction of therapeutic

exercises based on multimedia stimulation in real time. The clinical objective is to

experiment with a device of sensorimotor stimulation that supports akinesia compensation

by controlling movement rhythmic structures in subjects with Parkinson’s disease. The

EyesWeb open architecture has been used to analyse patients’ motion and to produce visual

feedback during therapy sessions in real time. A pilot study has been conducted on two

Parkinson’s disease patients in the framework of the EU IST project CARE-HERE.

Copyright # 2003 John Wiley & Sons, Ltd.

Received: 1 October 2002; Revised: 1 March 2003

KEY WORDS: therapy and rehabilitation; Parkinson’s disease; motion analysis; multimediatechniques

IntroductionIn the framework of the EU IST project CARE-HERE, we

have carried out experiments with the purpose of plan-

ning, developing and validating aesthetically resonant

environments for different types of sensorimotor im-

pairments which affect Parkinson’s patients.

The underlying idea of aesthetic resonance is to give

patients a visual and acoustic feedback depending on a

qualitative analysis of their (full-body) movement, in

order to evoke ludic aspects (and consequently intro-

duce emotional–motivational elements) without the

need for the rigid standardization required in typical

motion analysis labs, or invasive techniques: the subjects

are free to move in 3D with no sensors/markers on body.

The scientific community is addressing a growing inter-

est to the investigation and development of therapeutic

exercises based on interactive multimedia technologies.1–3

In the specific context of Parkinson’s disease (PD)

Albani and colleagues4 showed that virtual reality

(VR) can work as an effective external stimulus in order

to explore the motor plans by means of creation of

mental images. In this study, a VR environment has

been employed reproducing situations related to com-

mon daily activities at home, such as eating or using the

bathroom. The VR environment has been successfully

tested on two women with PD aged 68 and 69 years, and

on 10 normal control subjects.

Music therapy, including singing, music listening,

sharing and discussion of songs, learning to play instru-

ments, song writing, moving to music and participation

in music activities, also proved its effectiveness. For

example, Pacchetti and colleagues5,6 developed an ac-

tive music therapy programme combining both motor

and emotional rehabilitation for PD patients. Their

programme consisted of several group sessions, each

based on the correct ordering of listening tasks, exer-

cises and improvisation. Since significant differences

have been found among pre- and post-therapy assess-

ments demonstrating a significant improvement in hy-

pokinesia and in daily performances, the authors

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Copyright # 2003 John Wiley & Sons, Ltd.

*Correspondence to: Gualtiero Volpe, InfoMus Lab, DIST—Universita degli Studi di Genova, Viale Causa 13, I-16145,Genova, Italy. E-mail: [email protected]

Contract/grant sponsor: EU IST project CARE HERE.

propose active music therapy as a new method to be

included in PD rehabilitation programmes.

Our research tackles the rehabilitation task in both its

technical and clinical aspects. From a technical point of

view the aim is twofold:

1. To develop a computational open architecture in

which it is possible (i) to integrate modules for

gesture analysis and recognition and for real-time

generation of multimedia feedback, (ii) to design

dynamic and interactive therapeutic exercises, and

(iii) to perform the exercises in real time.

2. To develop algorithms for real-time motion analysis

that, despite the limitation given by the lack of on-

body markers/sensors, are reliable and precise

enough (i) to enable the generation of suitable audio

and visual feedbacks in aesthetically resonant

environments, and (ii) to allow the therapist to

evaluate the progress of the therapy by monitoring

the measures associated with a collection of motion

features the algorithms provide him.

The clinical objective is to experiment with a device of

sensorimotor stimulation that supports akinesia com-

pensation by controlling movement rhythmic structures

in subjects with PD. The training in recognizing and

reproducing these structures can help the subject to

control motor tasks that are more complex than those

adjusted with simple isochronous signal stimuli. A

major fluency in movement recovered with those meth-

ods can be transformed in visual or acoustic information

produced by the movement itself, which can guide the

internal representation of voluntary control.

This paper discusses a pilot experiment we carried out

in order to test the developed techniques on patients

with PD. Patients were chosen on the basis of a previous

experimental study developed at the hospital of Are-

nzano, Italy.7,8 The EyesWeb open software platform

(www.eyesweb.org) has been adopted as the basic fra-

mework in which the motion analysis techniques have

been integrated and the therapeutic exercises developed.

Materials andMethods

Subjects and Protocol

In a preliminary phase of the study we selected two

parkinsonian subjects: a man (aged 67) and a woman

(aged 73). The protocol consisted of 12 trials for the male

patient during a period of 6 months and six trials for the

female patient during a period of 2 months. During

every session, held weekly, the subjects were filmed

both during the execution of some simple physical

exercises and during audio-visual analyses developed

with the EyesWeb open platform.9 In these pilot studies

we used both standard material distributed with Eye-

sWeb and new exercises suggested by the physicians of

the operative unit, as, for example, an exercise in which

the subject is allowed to paint with his own body by

moving it in the space.

Starting from the considered symptoms (postural

instability, gait difficulties and bradykinesia) we se-

lected some simple tasks in order to provide a preli-

minary taxonomy from which rehabilitation and

evaluation exercises can be built. In particular, here we

show an example of implementation of the Stand Sit

Exercise also used in PD functional evaluation, as in the

UPDRS.10

The EyesWebOpen Software Platform

The EyesWeb open hardware and software platform9

(www.eyesweb.org) has been adopted for the design,

development and real-time performance of physical

exercises, the extraction of relevant motion parameters

and analysis of the obtained data.

EyesWeb is an open hardware and software platform

originally conceived for the design and development of

real-time music and multimedia applications. It sup-

ports the user in experimenting with computational

models of non-verbal expressive communication and

in mapping gestures from different modalities (e.g. hu-

man full-body movement, music) onto multimedia out-

put (e.g. sound, music, visual media). It allows fast

development and experiment cycles of interactive per-

formance set-ups by including a visual programming

language allowing mapping, at different levels, of

movement and audio into integrated music, visual and

mobile scenery.

EyesWeb is the basic platform of the MEGA (Multi-

sensory Expressive Gesture Applications) IST-20410 EU

Project (www.megaproject.org) and has also been

adopted in the IST CARE HERE Project on therapy

and rehabilitation. EyesWeb is fully available at its

website (www.eyesweb.org). Public newsgroups also

exist and are managed daily to support the growing

EyesWeb community (more than 500 users at the mo-

ment), including universities, research institutes and

industries.

In the particular framework of this study, EyesWeb

has been selected since it (i) allows interactive mapping

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Copyright # 2003 John Wiley & Sons, Ltd. 270 J. Visual. Comput. Animat. 2003; 14: 269–278

of motion parameters onto sounds and visual media in a

multimedia scenario, (ii) allows integration of novel

analysis techniques as new libraries or extensions to

existing libraries, (iii) allows fast design, development

and testing of novel interactive therapeutic exercises,

(iv) can display in real time the analysed physical

measurements, and (v) supports different types of sen-

sors (including wireless), one or more cameras, and can

be programmed to perform specific analysis of move-

ment in real time. For this last task, the EyesWeb

Expressive Gesture Processing and Motion Analysis

libraries11,12 have been employed, including software

modules for extraction and pre-processing of physical

signals (e.g. video from video cameras), and extraction

and processing of motion parameters (such as contrac-

tion index, directness index, stability index, quantity of

motion, pause and motion durations).

Extraction and ProcessingofMotionInformation

EyesWeb has been used both to (i) extract and analyse

relevant motion features, and (ii) produce aesthetically

resonant visual feedback. An archive of recordings from

therapy sessions has been collected for preliminary

testing and tuning of the developed exercises. Patients

have then performed the exercises during therapy ses-

sions with the system working in real time.

A layered approach has been followed to move from

low-level physical measures (e.g. position, speed, accel-

eration of body parts) towards descriptors of overall

motion features (e.g. motion fluency, directness, impul-

siveness) related to PD symptoms.

These high-level descriptors are grounded on both the

consolidated tradition of biomechanics and on studies

by researchers on human movement in the field of

performing arts, such as the choreographer and human

movement researcher Rudolf Laban and his Theory of

Effort,13,14 already used in computational models of

human movement.11,12,15,16

Motion Detection and Tracking. Incoming video

frames are processed to segment motion and no-motion

regions and to detect and obtain information about the

motion that is actually occurring. This task is accom-

plished by means of consolidated computer vision

techniques usually employed for real-time analysis

and recognition of human motion and activity (see, for

example, the temporal templates technique for repre-

sentation and recognition of human movement de-

scribed by Bobick and Davis.17). It should be noticed

that, unlike the research of Bobick and Davis, here we do

not aim at detecting or recognizing a specific kind of

motion or activity, but rather our goal is to calculate

values of cues able to numerically describe qualitative

aspects of motion (such as motion fluency). The techni-

ques we use include feature tracking based on the

Lucas–Kanade algorithm,18 skin colour tracking to ex-

tract positions and trajectories of hands and head, and

silhouette motion images (SMIs).

An SMI is an image carrying information about

variations in the silhouette shape and position in the

last few frames. SMIs are inspired to motion–energy

images (MEIs) and motion–history images (MHIs).17,19

They differ from MEIs in the fact that the silhouette in

the last (more recent) frame is removed from the output

image: in this way only motion is considered, while the

current posture is skipped. Thus, SMIs can be consid-

ered as carrying information about the amount of mo-

tion occurring in the last few frames. Information about

time is implicit in the image and not explicitly recorded.

We also use an extension of SMIs, which takes into

account the internal motion in silhouettes, and decom-

position of the silhouette into sub-regions (see Figure 1).

Information that motion detection and tracking pro-

vide to the upper levels is actually encoded in two

different forms: positions and trajectories of points on

the body (possibly related to specific body parts, e.g.

hands, head, feet), and images directly resulting from

the processing of the input frames (e.g. human silhou-

ettes, SMIs). Such information is used to extract a

collection of motion features. As examples, we describe

in the following two such features: quantity of motion

(QoM) and contraction index (CI).

Quantity of Motion. The simplest use of an SMI is

calculating its area. The result, which we call quantity of

motion, can be considered as an overall measure of the

amount of detected motion. QoM can be thought as a

first, rough approximation of the physical momentum,

that is, q¼mv, where m is the mass of the moving body

and v is its velocity. The shape of the QoM graph is close

to the shape of the graphs of velocity of a marker put on

a limb.

QoM has two problems: the measure depends on the

distance from the camera; difficulties emerge when

comparing measures from different patients. We solved

these problems by scaling the SMI area by the area of the

most recent silhouette:

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Quantity-of-Motion ¼ AreaðSMI½t; n�Þ=AreaðSilhouette½t�Þ

In this way, the measure becomes independent from the

camera’s distance (in a range depending on the resolu-

tion of the video camera), and it is expressed in terms of

fractions of the body area that moved. For example, it is

possible to say that at instant t a movement correspond-

ing to 2.5% of the total area covered by the silhouette

occurred.

Contaction Index. The contraction index is a mea-

sure, ranging from 0 to 1, of how the patient’s body is

using the space surrounding it. It is inspired by previous

studies of the authors on dance performances12 and is

related to Laban’s ‘personal space’.14

The algorithm to compute CI combines two different

techniques: the individuation of an ellipse approximat-

ing the body silhouette and computations based on the

bounding region. The former is based on an analogy

between image moments and mechanical moments: in

this perspective, the three central moments of second

order build the components of the inertial tensor of

rotation of the silhouette around its centre of gravity:

this allows computation of the axes (corresponding to

the main inertial axes of the silhouette) of an ellipse that

can be considered as an approximation of the silhouette:

eccentricity of such an ellipse is related to contraction/

expansion; orientation of the axes is related to the

orientation of the body.20 The second technique used

to compute CI relates to the bounding region, i.e. the

minimum rectangle surrounding the patient’s body. The

algorithm compares the area covered by this rectangle

with the area actually covered by the silhouette. Intui-

tively, if the limbs are fully stretched and not lying along

the body, this component of CI will be low, while, if the

limbs are kept tightly nearby the body, it will be high

(near to 1). While the patient is moving, CI varies

continuously. Even if it is used with data from only

one camera, its information is still reliable, being almost

independent from the distance of the patient from the

camera. A use of this feature consists of sampling its

values at the end and the beginning of a stretch move-

ment, in order to classify that movement as a contraction

or expansion. Suitable multimedia feedbacks can then

be mapped onto actions of contraction or expansion

depending on the therapeutic goals (e.g. pleasant music

can be associated with expansion, if therapy aims at

motivating the patient in doing expansion actions).

Motion Segmentation. SMIs have interesting prop-

erties: evolution in time of their (normalized) area (what

we called quantity of motion) resembles the evolution of

velocity of biological motion, which can be roughly

described as a sequence of bell-shaped curves (motion

bells). In order to segment motion, the sequence of these

motion bells and their features, e.g. peak value and

duration, have been extracted. A first attempt consists

of recognizing phases during which the patient is

Figure 1. (a) Multiple bounding regions. (b) Measure of internal motion using SMIs.

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moving (motion phases) and phases during which he or

she does not appear to move (pause phases). An em-

pirical threshold has been defined: the patient is con-

sidered to be moving if the area of the motion image is

greater than 2.5% of the total area of the silhouette.

Figure 2 shows motion bells after segmentation: a mo-

tion bell characterizes each motion phase.

Fluency and Impulsiveness. Motion segmentation

can be considered as a first step towards the analysis

of the rhythmic aspects of a patient’s motion. Analysis of

the sequence of pause and motion phases and their

relative time durations can lead to a first evaluation of

how much motion is even and regular, or hesitating.

Parameters from pause phases are also extracted to

individuate real still positions from active pauses invol-

ving low motion (hesitating or oscillating movements).

Starting from these data motion fluency and impul-

siveness are evaluated. Fluency can be estimated start-

ing from an analysis of the temporal sequence of motion

bells. An action (e.g. standing up) performed with

frequent stops and restarts (i.e. characterized by a high

number of short pause and motion phases) will be less

fluent than the same movement performed in a contin-

uous, ‘harmonic’ way (i.e. along a few long motion

phases). The hesitating, bounded performance will be

characterized by a higher percentage of acceleration and

deceleration in the time unit (due to the frequent stops

and restarts), a parameter that has been demonstrated to

be of relevant importance in motion flow evaluation

(see, for example, Zhao,16 where a neural network is

used to evaluate Laban’s flow dimension). A first mea-

sure of impulsiveness can be obtained from the shape of

a motion bell. In fact, since QoM is directly related to the

amount of detected movement, a short motion bell

having a high peak value will be the result of an

impulsive movement (i.e. a movement in which speed

rapidly moves from a value near or equal to zero, to a

peak and back to zero). On the other hand, a sustained,

continuous movement will show a motion bell charac-

terized by a relatively long time duration in which the

QoM values have little fluctuations around the average

value (i.e. the speed is more or less constant during the

movement).

Therapeutic Exercises

We developed a number of EyesWeb exercises for

experiments with PD patients. One of them, for one

patient at a time, allows the subject to paint using his or

her body.

The patient sees himself on a large screen painting in

real-time through his motion in the space. Previous

work in the performing arts field exists where engage-

ment of the audience is obtained by combined genera-

tion of music, sounds and visual media (see, for

example, the PAGe—Painting by Aerial Gesture sys-

tem21). With PAGe the user can interact through an

interaction paradigm like the MS Paint software, using

his hands while standing in front of a large video screen:

the user can select a colour or an action with one hand,

then can paint with that colour with the other hand, etc.

Our exercise is slightly different: the interaction is

based on some of the movement cues described in the

previous sections. For example, the colour may depend

on fluency; QoM may be associated with intensity of the

colour trace; pauses in movement (using the segmenta-

tion technique previously described) allow restarting

the process and reassigning/adapting interaction map-

pings. In this way, by a careful choice of colours, e.g. by

creating ‘pleasant’ colour associations/mappings with

fluent and non-hesitating movements, it is possible to

create a sort of visual feedback encouraging improve-

ment of movement in patients. During this exercise the

subject looks at the picture painted on the screen and

continuously changes it while moving.

On another monitor the researcher analyses the para-

meters and eventually corrects them in order to tune the

exercise on the patient’s needs. Figure 3 shows some

excerpts from a session with a patient.

Several other exercises, involving integrated audio

and visual feedback, are currently the subject of inves-

tigation. In one of them we analysed fine movements, in

particular hand recognition and position (Figure 4). The

EyesWeb system allows extracting fine movements in a

precise manner and also detects the movement intention

by recognizing arm movement in order to be easily

suited to severely impaired persons.

Figure 2. Motion segmentation.


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We also analysed simple tasks in order to provide a

preliminary taxonomy from which we may design

rehabilitation and evaluation exercises. In particular,

here we show an example of implementation of the

stand–sit exercise also used in PD functional evaluation

as in the UPDRS (Figure 5).

The challenge and the aim of our preliminary study

was to evaluate if such kinds of systems are able to

generate aesthetically resonant feedback in PD patients,

providing the emotional/motivational involvement to

allow them at least partially to overcome their disease.

This will be the subject of a future clinical study.

At the moment, however, an analysis of a question-

naire about Quality of Life that the patients filled in after

each therapy session (12 trials for the male patient

during a period of 6 months and six trials for the female

patient during a period of 2 months) demonstrated an

overall positive trend and an average increment of

Figure 3. The painting exercise.

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Figure 4. Analysis of hand movements.

Figure 5. Stand–sit exercise.


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patients’ satisfaction that moved from 33% at the begin-

ning of the therapy to 60% after it.

Discussion

The pilot experiments demonstrate the feasibility of the

project: patients have understood in a natural way the

dynamics of the proposed resonant environments and

they obtained benefits from them, if nothing else, from a

motivational point of view. The acquisitions made dur-

ing the interactive exercises highlighted a general im-

provement of movement fluency, and appropriate

EyesWeb patches for automatic analysis will be devel-

oped. Of course, this is only a preliminary study because

it could be applied only to a very limited population of

patients. However, the experience was essential for

understanding the practical problems that could allow

the organization of a clinical trial in the near future. In

particular, we defined the environmental requirements,

the acquiring protocol and some guidelines for design-

ing the proper exercises for analysis and improvement

of movement rhythmic structures.

Overall, the EyesWeb architecture demonstrated great

versatility, giving a new possibility to the physician to

focus exercises and analyses on the desired rehabilitative

target and to evaluate the efficacy of the therapy.

ACKNOWLEDGEMENTS

The work described in this paper has been partially supported

by the EU IST project CARE HERE. We thank our colleagues

Riccardo Trocca and Matteo Ricchetti at the InfoMus Lab,

physicians Luigi Baratto and Marcello Farinelli, Psiche Gian-

noni and Emanuela Cervetto, Grazia Ornato, Luciana Tabbone

and Alessandro Tanzini at CBC for their contribution to this

work. We also thank the EyesWeb development team (Paolo

Coletta, Massimiliano Peri and Andrea Ricci) for their support.

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21. Tarabella L, Bertini G. Wireless technology in gesture con-trolled computer generated music. In Proceedings of theWorkshop on Current Research Directions in Computer Music,Barcelona, 2001.

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Authors’biographies:

Antonio Camurri is Associate Professor at University ofGenova where he teaches the courses of ‘Software en-gineering’ and ‘Multimedia Systems’ (Computer Engi-neering curriculum). He is the founder and scientificdirector of the Laboratorio di Informatica Musicale atDIST, member of the Board of Directors of AIMI (ItalianAssociation for Musical Informatics), member of theExecutive Committee (ExCom) of the IEEE CS TechnicalCommittee on Computer Generated Music, foundingmember of the Italian Association for Artificial Intelli-gence (AI*IA), Associate Editor of the internationalJournal of New Music Research (Swets and Zeitlinger).He is author of more than 70 scientific internationalpublications, and served in the scientific committees ofseveral international conferences. He is owner of patentson software and computer systems, algorithms andmultimedia systems. He is Chairman of the followingscientific events: ‘First Intl Workshop on Kansei—TheTechnology of Emotion’, Genova, October 1997, AIMIand IEEE CS. Track on ‘Kansei Information Processing’at the IEEE Intl Conf SMC’98, San Diego (3 ScientificSessions). Track on ‘Kansei Information Processing’ atthe IEEE Intl Conf SMC’99, Tokyo, co-chair of the 5thIntl. Gesture Workshop, and Guest Editor of a specialissue of IEEE Multimedia J. on ‘Multi-sensory commu-nication and experience through multimedia’ (Jul–Sep2004).

Barbara Mazzarino was born in Genova, Italy, on the30th of July 1976. She received her Laurea degree inComputer Engineering from the University of Genovaon April 23rd 2002, with a thesis on analysis of expres-

sive gesture in human full-body movement. Now she isa PhD Student in Computer Science Engineering andshe works at the DIST—InfoMus Lab of the Universityof Genova, directed by Prof. Antonio Camurri. Sheworks on the modeling and real-time analysis andsynthesis of expressive content in dance and humanmovement.

Gualtiero Volpe computer engineer, was born in Gen-ova on March 24th, 1974. He received his degree incomputer engineering from the University of Genova in1999. He will discuss his PhD dissertation in June 2003.His research interests include intelligent and expressivehuman-machine interaction, modeling and real-timeanalysis and synthesis of expressive content in musicand dance, KANSEI information processing and expres-sive gesture communication. At the moment he is re-searcher at the DIST—InfoMus Lab at the University ofGenova. He is co-chair of the 5th Intl. Gesture Workshop(Genova, April 15–17, 2003).

Pietro G. Morasso was born in Genova, Italy, on April30, 1944. In 1968 he received his Laurea degree inElectronic Engineering cum laude from the Universityof Genova. He has been associated with the MIT Depart-ment of Psychology, Cambridge, Mass. USA, in theNeurophsysiological laboratory of Prof. Emilio Bizzi asa post-doctoral fellow (1970–1972), Fullbright fellow1973, vsiting professor 1978, 1979, 1980. He had differentpermanent positions in the Engineering Faculty of theUniversity of Genova since 1970. Currently he is fullprofessore of Anthropomorphic Robotics and chairmanof the Laurea Programme in Biomedical Engineering.


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He is founder and scientific director of the Bioiengineer-ing Center at the Rehabilitation Hospital La Colletta inArenzano since 1995. His scientific interests include thefollowing topics: analysis of the motor control system;computational neuroscience; neuroengineering; anthro-pomorphic robotics; rehabilitation engineering. He isauthor/coauthor of 6 books, over 300 papers, and twopatents (one on robotic navigation and one on laser-therapy).

Federica Priano was born in Genoa on October 25, 1966.She received her degree in Psychology at the Universityof Padua in 1991 and was licensed as a Psychotherapistin 1999. In 1997, she received her PhD in ‘Psychody-namic and Neurophysiology’ at the University of Genoadiscussing a thesis on ‘Cognitive Rehabilitation’. Shefound the Laboratory of Clinical and RehabilitationNeuropsychology in the Rehabilitation Department(Chairman Dr. Baratto) of ‘La Colletta’ Hospital inArenzano (Genoa) where she dealt with Neuropsycho-logical Assessments and Cognitive Rehabilitation pro-grams to patients with neurological diseases (stroke,Parkinson’s disease, Multiple Sclerosis, traumatic braininjury, tumors, dementia, etc). She also cooperates withthe Bioengineering Center at the same Hospital. She isauthor/co-author of 19 scientific publications.

Cristina Re was born in Albenga, Italy, in 1971. Shereceived her degree in electronic engineering from Uni-versity of Genoa in 1999 with the thesis: ‘Analysis andclassification of posturographic parameters by neuralnetworks computation’ supervised by prof P. G.Morasso. Currently she is PhD student in Bioengineer-ing and Bioelectronic at University of Genoa and attendsher researches in the staff of prof. P. G. Morasso and MDL. Baratto at the Centre of Bioengineering—‘La Colletta’hospital—Arenzano. Her research interests includePosturographic and Surface Electromiographic DataAnalysis, Motion Analysis, Neural Networks. She hasalso been involved in the project CAREHERE financedby the European Community.

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application of multimedia techniques in the physical rehabilitation of parkinson's patients

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