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A Computer-Aided Movement Analysis System SANDRO FIORETTI, TOMMASO LEO, MEMBER, IEEE, EDOARDO PISANI, AND M. LETIZIA CORRADINI

Abstract-Interaction with biomechanical data concerning human movement analysis implies the adoption of various experimental equipments and the choice of suitable models, data processing, and graphical data restitution techniques. The integration of measurement setups with the associated experimental protocols and the relative software procedures constitutes a computer-aided movement analysis (CAMA) system. In the present paper such integration is mapped onto the causes that limit the clinical acceptance of movement analysis methods. The structure of the system is presented. A specific CAMA system devoted to posture analysis is described in order to show the attainable features. Scientfic results obtained with the support of the described system are also reported.

I. INTRODUCTION OVEMENT analysis (MA) is the set of methods and techniques aimed at a quantitative assessment of human movement. Through such an assessment some contribution to the understanding of the laws governing the motor behavior can also be given [1]-[3]. Movement analysis is based on the assumption that the human body can be modeled as a kinematic chain of articulated rigid bodies. Consequently, kinematics is supposedly known when the position of a suitable number of markers placed on human anatomical landmarks is known, or when the angles between contiguous segments are measured. Dynamic variables, such as intersegmental forces and couple vectors, are calculated by means of mathematical models based on the rigid body mechanics [4]. Typical instrumentation is constituted by stereophotogrammetric or electrogoniometric systems for the recording of kinematics [5]-[ 111, by force platform for the measurement of ground reaction forces [12]-[14], and by electromyographic systems for the measurement of muscle activity [15]-[18]. Fields of interest of MA are kinesiology, ergonomics, sports medicine, and obviously rehabilitation where MA deals with the functional assessment of motor diseases. But in this latter field MA has received a limited clinical acceptance at least in Europe. The causes that determine such a situation can be summarized as follows [19]:

M

1) Misunderstanding about the application domain of MA. Manuscript received July 28, 1989; revised January 12, 1990. This work was supported under the CAMARC project of the European Communities, AIM (Advanced Information in Medicine) program, and supported by the Italian National Council of Research (CNR), MPI 4 0 % funds, and a research grant by USL 28 (Bologna Nord). The authors are with the Dipartimento di Elettronica e Automatica, Universita degli Studi di Ancona, 60131 Ancona, Italy. IEEE Log Number 9036242.

2) Lack of (and difficulties in building) an accredited knowledge base from quantitative MA findings. 3) Perplexities about the reliability of MA methods and techniques in managing relevant and intrinsic measurement inaccuracies. 4) Claims against the validity of current MA techniques for assessing impairments and concomitant inhabilities. As far as points 1) and 4) are concerned, we must say that MA is mainly a tool for quantitative, functional movement assessment of diseases usually already diagnosed. But MA can be useful also in clinical decision making and in monitoring the effects of conservative and surgical treatment. Furthermore, the current MA methodologies seem capable of assessing the impairment but not the disability, mainly because of the clinical protocols that are usually adopted. With reference to point 2) it can be stated that many significant results have been obtained both in research and in clinical contexts. However, this wealth of information has difficulty to be put into practical use, largely for the lack of standardization in clinical and experimental protocols. Clinical protocols have to be agreed with by the various groups involved in the study of the same kind of motor disease. Experimental protocols are, up to now, largely dependent on the specific MA instrumentation used. In particular, optoelectronic kinematic data acquisition systems are frequently equipped with proprietary data processing procedures. Consequently, data are poorly or not at all communicable among the various laboratories, even in a context of close cooperation, so that up to now a concerted knowledge base was unable to coalesce. We can say on point 3) that, while the bioengineering methodology is continuously improving, its transfer towards the clinical environment is difficult mainly because of the lack of common knowlege and language. In the present paper the attention is mainly focused on two of the above problems: the lack of a concerted and accredited knowledge based on MA quantitative findings; the difficulty of transferring towards the clinical environment the improved methods, able to overcome problems of reliability, made available by bioengineering. We think that the development of a computer-aided movement analysis (CAMA) system can be useful to solve the problems. A CAMA system can be thought as a software tool integrating the measurement setups with the associated experimental protocols and the relative compu-

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tation procedures. In particular we think of a CAMA system that has the following characteristics: it is specific for each kind of motor disease; it implements predefined (and hopefully standardized) experimental protocols that can be applied at the various MA laboratories, independently of their instrumentation; it is self-explanatory, in the sense that it is constituted by self-documenting programs, able to justify their suggestions to the user, allowing an easy interaction with the large amount of data that every MA experiment gives rise. In the following the CAMA system we are developing for the assessment of postural stability of subjects suffering from multiple sclerosis will be described. An MA-based clinical knowledge is lacking for such a disease, and no standardized MA protocols exist for the evaluation of its effects on postural stability. Accordingly, the design of the present CAMA system has been carried out in a research context, while in close connection with the clinical environment. Great care has been given to a friendly interaction with data, and to an easy navigation along the proper software procedures for postural analysis, so that both technical and clinical researchers could benefit by the use of the CAMA system. The achievement of such goals has been obtained by the development of a software tool we named “friendly interface” (FI). As it will be shown in the following, the possibility offered by the CAMA system of having “on-line” the entire set of results for every experiment, and the possibility of graphically representing whatever variable available in the database has contributed to the deeper understanding of the organization of postural control and of its modifications in the case of preclinical multiple sclerosis. 11. POSTUREANALYSIS:PROBLEMSTATEMENT The traditional clinical protocols relative to postural stability mainly refer to the study of the Romberg’s sign [20]. The relevant results are useful to put into evidence the effects of postural control, but are not able to show its internal structure and organization. The Romberg’s sign in fact, showing the displacement of the upright whole body center of pressure, is simply able to put into evidence geometric patterns that are characteristic of already diagnosed pathologies. In the current clinical practice the study of the Romberg’s sign is mainly qualitative. In order to increase the resolution power of such a study, classical but not trivial DSP methods like the frequency analysis have been used [21], [22]. To gain a deeper insight into the mechanisms of posture maintenance, what we may call the controller of the posture control system [23]-[26], more complex protocols have to be adopted to feed mathematical models with the necessary data. In general three basic steps are taken: a model is established for the standing upright subject;

meaningful data for the chosen model are acquired from the subject; data are processed through algorithms whose complexity is adequate to the level of model detail. The proper modeling of the subject is the crucial step in the above sequence. Starting from a simple inverted pendulum description, the level of model detail can be increased to a multilink kinematic chain. Whatever the level of detail is, our attention is focused on the identification of the feedback character of the posture control system. In this case purposeful identification procedures [27]-[29] and sophisticated filtering procedures of the raw data [30] are required too. Aiming at the clinical application of the MA techniques for the study of postural stability, the problems to be faced are the following, in large part common to all the clinical applications of MA techniques: 1) transparency between user and instrumentation;

2) ease in the choice of the proper protocols in relation to each experiment; 3) ease in the choice of the proper DSP algorithms; 4) management of a large quantity of data per each experiment; 5 ) ease in the expansion towards new application procedures.

1) Following the traditional clinical protocols only a force platform is needed. When the target is to gain insight into the posture controller, kinematic data are also needed; moreover, the number of body segments to be considered changes according to the level of detail of the adopted model. Finally, the use of a proper set of EMG records can be also foreseen. A rapid, reliable, and simple transition. among the different experimental setups has to be allowed to the clinical user. 2) When various protocols can be used in relation to the same pathology, according to the target of each experiment, it is important to have a reliable information and a sound verification about what protocol is being used; moreover, the relevant information has to be permanently recorded together with the captured data for a correct future retrieval. 3) Different protocols correspond to different models and they could imply different data processing procedures. It is convenient for the MA practicer, particularly in a research context, to have the possibility of choosing the proper DSP algorithms among a reduced set of selected, appropriate algorithms. This advantage will be as powerful as the features of each algorithm will be clearly and friendly communicated to the user. 4) In general, MA techniques give rise to a large amount of data per each experiment, both directly kept through the measurement systems and calculated by suitable algorithms. The functional assessement of the subject’s motor performance implies the synthesis of all the relevant information. This work could be greatly facilitated by the availability of a graphic display of any vari-

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able versus any other, and of the simultaneous display of many plots, representing the same variable(s) relative to different homogeneous experiments. 5 ) Such a problem depends on the lack of a concerted clinical knowledge and on the continuous improvement of the bioengineering methods relevant for MA. Both these circumstances ask for the possibility of an upgrading of the available procedures and of the increase of their number. ANALYSIS 111. THE CAMA SYSTEMFOR POSTURE

The CAMA system we are developing allows to approach the problem of posture analysis following both the traditional and the new protocols. Its FI makes it flexible enough to manage all the above mentioned problems, and allows communication between the user and the system through the employment of window-technique-based menus. The FI is the hardware-independent tool that allows the navigation through the various procedures according to different experimental protocols, and helps the user in managing instruments and data bases. Roughly speaking, the CAMA system can thus be considered as constituted by a common structure (i.e., the FI), which is unique for the various implementations and which is particularized by the introduction of the proper set of procedures relative to the disease-dependent protocols. The solution given to the above mentioned problems are as follows: 1) Transparency towards instrumentation has been attained by the development of one driver for each instrument. In our case the measurement systems adopted are: an automatic stereophotogrammetric system (CoSTEL) using infrared light emitting diodes (LED’s) as active markers [ I l l , [31]; an extensimetric force platform [14] connected to an A/D converter. Both instruments are connected to an IBM-compatible personal computer. 2) In the CAMA system, each time an experiment is performed, the user has to select the desired “experimental configuration” among a set of predefined ones, in order to attain to a specific experimental protocol. Being each experiment associated to the relative protocol, the entire set of results is subdivided into classes, so that the user is warned in performing comparisons among trials belonging to different classes. 3) For each experimental protocol, FI allows the user to follow the correct sequence of procedures: the list of executable procedures at each processing level is shown in a window (see Fig. 1). As far as data filtering is concerned, simple classical filters can be used [ 3 2 ] , [33] when dealing with classical clinical evaluations, while in the case of the identification of the controller structure more refined and suitably developed smoothing procedures [301 have to be employed. Consequently, in the former case the FI gives the user the possibility of choosing among

Fig. 1 . A typical menu of the friendly interface. It is relative to the experiment selection phase. In the left window the list of the available experiments is shown, while in the right one the relevant information about the highlighted experiment is reported.

various filtering techniques, while in the latter case the system automatically selects the most appropriate one. Each filter is anyway made available to the user, together with the explanation of its characteristics. A further support is provided by the CAMA system during certain critical phases of the computing sequence, particularly in the identification of the controller of the feedback posture control system. In our case, a simple inverted pendulum model of the standing upright subject in the sagittal plane has been adopted up to now [23], [25], [26], [34]. The controller of the posture control system has been modeled by an ARMAX process having the subject’s body sway as input, and the moment at the ankle as output [25], [34]. The identification of the model parameters is carried out using the recursive prediction error method [27], [28], [29]; the model order choice is the critical point of the procedure, and requires the user intervention in the evaluation of the results of the data fitting to the model. To this purpose, a software tool has been developed to provide a support for such a choice. This tool tests the system being identified, forcing upon it different ARMAX model orders. It uses some widely recognized criteria [27], 1291 to verify the success of the estimation procedure, and to reduce or to increase the model order from a set of initial values on the basis of classical tests performed on the residuals of the estimation. The user can accept or refuse the suggested value of the model order, having available all the results of the performed tests. 4) In the case of the experimental protocol adopted for the identification of the controller, each subject is analyzed while performing four different tasks (a cognitive spatial task, a cognitive verbal task, stance with closed eyes, and stance with open eyes [34]-[36]). Per each task, the position of ten markers and force platform data are recorded for a period of 20 s at a sampling frequency of 50 Hz, giving rise to 36 000 data, 2 bytes each. Starting

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from acquired data, many other kinematic and dynamic variables are computed. The architecture of the FI allows the user to access and to visualize whatever variable available in the database. Moreover, he has the possibility of comparing results belonging to homogeneous sets of trials, and of inquiring the database according to selected research keys. The realization of the above features has been obtained interfacing the FI with DB IV. 5) The architecture of the FI allows the introduction of new procedures as soon as they become available. At present the creation of new modules relative to a multilink model of the human body and to the corresponding multiinput, multioutput controller is foreseen. In the following, details will be given about the FI architecture and implementation.

A. Friendly Interface: Structure The design of the FI has been carried out with reference to the Seeheim model of an user interface [37]-[39], which divides it into three separate components: the presentation component (PIC), the dialogue control component (DCC), and the application component (AC). In our case a fourth component is present: it is a database of clauses for MA data processing (MADB, see Fig. 2). The PIC supports the interaction between the user and the system by managing the input-output devices. The DCC is the core of the FI that manages the interaction between the presentation and the application components, using the MADB. The AC is the interface through which the user accesses the application procedures. The PrC and DCC are completely developed using the Prolog language, whereas the AC has been developed using also other high level languages (Basic, Fortran, Pascal, and C ). Presentation Component: The PrC is responsible for the I/O management, the information display, the implementation of the user-computer interaction. Using a menu interaction style and the window technique, this module displays a list of available functions, which is sent to the PrC by the DCC. When the user has selected an item from a menu, the PC generates an input token which is sent to the DCC. Dialogue Control Component: The DCC controls the correct selection of the sequence of procedures which can be activated at each processing level. This component manages the single experiment data as an object which can be in different states Sj, j = 1, 2 , , n according to the content of the MADB and to the current phase of data processing. Several computation procedures are appended to each state and an experiment E , which is in the state S,, may be processed by all the procedures pertinent to it. Some procedures may change the experiment state and new actions are then made available. Application Component: The AC is composed by all the acquisition, preprocessing, processing, database management, and graphic restitution procedures. The AC calls the routines for the execution of the requested procedure

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Fig. 2. Friendly interface architecture.

on the basis of the token generated by the PrC. This token is verified and sent to AC by the DCC. MADB: This database is constituted by a set of clauses: each clause asserts a fact relative to a Prolog relation. Three different groups of relations are defined:

1) relations for representing a particular experimental protocol, i.e., the body segments involved in the movement and the position of the markers placed on those segments; 2) relations for supporting the user in the choice of the correct sequence of procedures: 3) relations for data management. The various protocols differ each other for the body segments involved in the particular movement, for the position of markers on each body segment, and for the set of procedures activable for data processing. The database is subdivided into different files, each of which contains the clauses about the pertinent protocol. A brief description of the most significant relations is now given. Group 1 stick ( P , Ns, D , LC, L D ) . The relation describes the various segments that form the stick figure representation of the subject. P is the protocol name, Ns is a body segment number, D is an item which describes the segment, LC is a list of coordinates for the stick figure display, LD is a list of the markers belonging to the NS segment. The various clauses relative to the above relation are used for providing at every moment a graphical restitution of the body segments and of the positioned markers. Group 2 elaboration ( S , LD, LP ). This relation asserts that it is possible to apply the procedure set LP to an experiment if this latter is in the state S . LD is an item description list which is displayed for the user selection procedure. transition ( Si, P, Sf ). The relation asserts that if the state of the experiment is Si and the procedure P is executed, the experiment state changes from Si to Sf. Group 3 listexp ( L E ). The list LE of the experiment names is defined by this relation. experiment ( E , D I , D2 0,). For each experiment E this relation defines its relevant data Di(i.e., date and

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I

WORKING

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Fig. 3. Block scheme of a typical FI session.

Fig. 4 . Stick figure representing marker positioning associated to a particular experimental protocol for posture analysis.

time of experiment, sampling frequency, number of Samples and so on). state(E, S ) . This relation asserts that the experiment E is in the state S .

B. Friendly Interface: Operations A typical FI session can be described by the block scheme of Fig. 3. The dark shadowed blocks symbolize the functions performed by the PrC, the light shadowed ones stand for-the functions performed by the AC, while the blocks with no shadow represent operations performed by the DCC.

Each FI session begins with the selection of the desired experimental protocol; consequently, the clauses are loaded by the DCC from the relative file. Such clauses become thus part of the FI. Two operations are now available to the user: i) acquisition of a new experiment; ii) selection of an already acquired working experiment . In the operation i), the system displays a stick figure by using the relation stick (see Fig. 4). The body segments are highlighted together with the markers posed on them.

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The DCC activates the acquisition driver, sending to it the needed parameters (number of LED’s, number of A/D channels of the force platform, sampling frequency). In the operation ii), the user selects an already acquired experiment from a list displayed by the PrC using the clause listexp, and the FI follows its normal way of functioning. The DCC retrieves the state of the selected experiment by means of the clause state. Then the activable procedures are selected by using the clause elaboration. The list of the descriptor items is made available to the PIC; it generates the relative menu and also allows the user to choose the desired computing procedure. The PrC sends to the DCC a token, which is generated according to the user selection of the desired procedure. The DCC receives the indication of such a procedure, and allows the AC to make it running. Once the computing phase is ended, the DCC uses the relation transition to eventually modify the state of the working experiment. At every moment, pressing some function keys, the user can change the working experiment, or he can eventually perform a new session selecting a different experimental protocol. The fact that Prolog can read clauses that become part of the calling program itself allows an easy particularization of the CAMA system to different motor disorders and to different protocols. IV. APPLICATION OF THE CAMA SYSTEMFOR POSTURAL CONTROL The CAMA system was applied to the postural analysis of 28 healthy subjects and of four pathological ones while performing four different tasks: a cognitive spatial task, a cognitive verbal task, stance with open and closed eyes [34]-[36]. Three of the pathological subjects were affected by multiple sclerosis (MS) at a very early stage, so that their posture performance did not differ appreciably from the healthy behavior. The fourth subject was suffering from a cerebral lesion of focal nature. Experimental data were processed according to classical time and frequency procedures, and were also analyzed in order to identify the controller of the posture control system [34]-[36]. The reported results refer to this latter aspect, which is the most significant from the point of view of the present paper. The standing upright subject has been modeled as a single inverted pendulum in his sagittal plane [23], [25], [26], [34]. We assumed that equilibrium is maintained by the subject by applying a moment at the ankle joint. Thus, the only controlling input is the ankle joint moment and the subject’s body sway can be considered as the controlled variable in the chosen model (see Fig. 5 ) . Black box modeling and identification has been applied to the controller block of the posture control system. It is well-known that multiple sclerosis impairs the postural equilibrium performance of the subject. Consequently, as far as questions about postural stability are concerned, it seems meaningful, once estimated the AR-

P-EH Js-m h

Fig. 5. Block scheme of the posture control system. The controlling input U is the ankle joint moment, while the controlled variable y is the body sway. M is the body mass, h is the height of the center of gravity, J is the moment of inertia of the whole body with respect to a horizontal axis belonging to the frontal plane at the ankle level.

Fig. 6. Poles distribution on the { - w, constant locus for the closed-loop posture control system. The numbers shown refer to tasks which subjects are engaged with during an experimental session. According to literature, cognitive verbal and visuo-spatial tasks have been hypothesized as potentially able to elicitate preclinical behaviors, and show stablizing effects on posture regulation. On this basis, subjects are evaluated in four different conditions: 1) cognitive visuo-spatial task; 2) cognitive verbal task, 3) open eyes; 4) occluded vision.

MAX parameters of the controller, to consider the whole closed-loop control system, whose global behavior is provided by the closed loop transfer function poles. The analysis of the number and of the position of such poles over the - U,,constant locus can provide information about the dynamic response of the posture control system (see Fig. 6). Results [34] obtained from our analysis can be summarized as follows: a) Differences in the model order of the controller were found between normal and pathological subjects. The tested healthy population showed a common behavior in all cases: the model order did not change while performing the four tasks nor among different subjects. On the contrary, the three tested MS subjects were found to be characterized by a different ARMAX model order for the controller. Moreover, it varied among the three subjects, so that at first we couldn’t recognize a common behavior for them. To establish the selectivity of the performed analysis with respect to the particular disease, tests are currently being performed on subjects suffering from ce-

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rebral lesion of various nature. Our first results, obtained on a hemiplegic (the reported fourth subject), seem to confirm that the modification in the model order of the controller is specific for multiple sclerosis. b) In all the tested cases, the cognitive task showed a stabilizing effect, if compared to stance with closed and open eyes. In Fig. 5 an example of the poles distribution of the closed-loop transfer function is shown. Furthermore, in the greatest part of cases, for both healthy and pathological subjects, the cognitive spatial task was found more destabilizing than the verbal one, that is the poles of the closed-loop system were nearer to the region of instability with respect to those related to the other cognitive task of the same subject.

V. DISCUSSION With respect to the causes that hinder the diffusion of MA methods in the clinical practice, the present approach helps in facing at least two problems cited in the “Introduction. ” The CAMA system has been mainly conceived as a preliminary step towards a unified approach to the MA study of a particular motor disease, and it has been thought as a mean to bridge the gap between research and clinical environment. In particular, the described CAMA system for posture analysis proved itself very useful as far as the interaction with clinicians was concerned. In fact, the clinical user was relieved of the burden related to a deep understanding of the technical aspects of the computing procedures, so that it was possible to focus the attention mainly on the interpretation of the results of the experiments. Moreover, the easy interaction with the whole set of results relative to the entire database of experiments, gave us the opportunity to discuss the bulk of problems with the clinicians, thus enabling the formulation and the verification of hypotheses and the opening of new research horizons. Besides the interaction with clinicians that allowed the foundation of a common language between researchers of different cultural extractions, the authors were also greatly helped by the CAMA system in the achievement of the scientific results of their research work. For example, the possibility offered by the system of interacting with data and procedures during the phase of controller identification, allowed an easy way to judge the choice of the model order automatically selected by the system. Furthermore, the database queries based on selected research keys, such as number and value of the poles of the posture control system, allowed an easy finding of the reported characteristics between normal and pathological subjects. The CAMA is a software tool coded in high-level languages (with the exception of the drivers for data acquisition). This fact assures its portability towards many computing systems. The choice of a personal computer as the hardware support for the system has been motivated principally for the widespread diffusion of such a machine in almost every research and clinical sites. Moreover, number of commercial software packages are disposable,

making it easier the smart solution of some specific problems such as those dealing with database management. At the same time the networking with mainframes is also allowed. Thus, the fulfillment of more complex tasks such as those required for a multilink modeling is not impeded. Unfortunately, the above choice poses problems for sophisticated computer graphic restitution. Although computer graphics is an effective mean to visualize and help the understanding of complex problems in many different contexts, few graphic programs have been developed for biomechanical analysis, all of which require powerful computers to run [40]. The development of the described CAMA system is in progress. In the future we foresee the necessity of adopting a more refined model of the standing upright subject in order to have a deeper insight into the organization of the posture control system. We think of a multilink model of the subject and of the identification of the associated multiinput, multioutput controller of the closed-loop system. A longer interaction with our clinical co-workers is needed in order to build up a consistent knowledge base using the quantitative results of the posture analysis. The characteristics of the CAMA system, in particular the use of an AI language for its implementation, might possibly allow to imbed in it the inferences that will be defined through such an interaction. ACKNOWLEDGEMENTS This is a paper under the CAMARC-project of the European Communities in the context of the AIM (Advanced Information in Medcine) program. The research program, whose results are partially reported, is supported by the Italian National Council of Research (CNR), by the MPI-40% funds and by a research grant financed by USL 28 (Bologna Nord.)

REFERENCES [ l ] N. Bernstein, The Coordination and Regulation of Movement. Oxford: Pergamon, 1967. [2] V. Inman, H. Ralston, and F. Todd, Human walking. Baltimore, MD: William and Wilkins, 1981. [3] D. H. Sutherland, R. A . Olshen, E. N . Biden, and M. P. Wyatt, The Development of Mature Walking. Blackwell: Oxford, 1988. [4] W . Braune and 0. Fischer, The Human Gait, P. Maquet and R. Furlong, Translators. Berlin: Springer-Verlag. 1987. [5] A . Cappozzo, T . Leo, and A . Pedotti, “A general computing method for the analysis of human locomotion,” J . Biomechan. vol. 8, pp. 307-320, 1975. [6] H. J. Woltring, E. B . Marsolais, “Optoelectronic (SELSPOT) gait measurement in two and three dimensional space. A preliminary report,” Bull. Prosth. Res., vol. 17, pp. 46-52, 1980. [7] I. S. Chang, S. H. Koozekanami, and M. T . Fatchi, “Computer television interface system for gait analysis,” IEEE Trans. Biomed. Eng., vol. BME-22, p. 259, May 1975. [8] M . 0. Jarret, B. J. Andrews, and J . P. Paul, “A television computer system for the analysis of human locomotion,” presented at IERE Conf. Proc., vol. 34, pp. 357-370, 1976. 191 D. A . Winter, R . K. Greenlaw, and D. A . Hobson, “Television computer anlaysis of kinematics of human gait,” Comput. Biomed. R e s . , vol. 5 , pp. 498-504, 1972. [lo] G. Ferrigno and A . Pedotti, “ELITE: A digital dedicated hardware system for movement analysis via real-time TV signal processing,” IEEE Trans. Biomed. Eng., vol. BME 32,. pp. 943-950, Nov. 1985.

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819 1341 M. L. Corradini. S . Fioretti, T. Leo. R. Piperno, M. Franceschini, and S . Stecchi. “Identification of human postural control.” IFAC 1990, Tallin, USSR. 1351 S . Stecchi, R. Piperno, M. Franceschini. T. Leo, and S . Fioretti, “Preliminary data~aboutthe influence of cognitive processing on postural control of standing,” presented at IX Int. Symp. Int. Soc. Postural and Gait Research: Development, Adaptation and Modulation of Posture and Gait, May 29-June I , 1988, Marseille, p. 45. T . Leo. S . Fioretti, M. Franceschini, R. Piperno, and S . Stecchi. “Clinical evaluation of postural sway,” presented at 111Europ. Conf. Res. Rehab., Rotterdam, June 8-10, 1988, p. 197. G. Pfaff and P. J . W. ten Hagan, Seeheim Worshop on User Interface Management Systems. Berlin. Springer-Verlag, 1985. M. Green, “The University of Alberta user interface management system,” Comm. ACM, vol. 19. no. 3 , pp. 205-213, 1985. H. Dickhaus, S . Fioretti, J. Freise, T. Leo, and E. Pisani, “A friendly interface for human movement studies,” presented at Conf. Medical Informatics 88: Computers in Clinical Med., Nottingham, Sept. 1315. 1988. pp. 233-238. S. Delp, F. Zajac. D . Delp, and P. Loan. ”A computer graphic system to study human movement.” presented at Proc. XI1 Int. Cong. Biomechan.. Los Angles, CA, June 1989, p. 169.

Sandro Fioretti, for a photograph and biography, see p. 409 of the April 1990 issue of this TRANSACTIONS.

Tommaso Leo, (M’88), for a photograph and biography, see p. 409 of the April 1990 issue of this TRANSACTIONS.

Edoardo Pisani was born in Pisa, Italy, in 1951 He received the Doctor degree of Computer Sciences from the University of Pisa, Pisa, Italy, in 1975 From 1975 to 1985 he was a Researcher at the Department of Internal Medicine of Ancona University and his research interests concerned the automatic monitonng of ECG Since 1985 he has been a Researcher at the Department of Electronics and Automatica, University of Ancona, Ancona, Italy His research in human computer interaction aspects includes the development of intelligent and user friendly interface for digital signal processing in computer-aided movement analysis and computenzed EEG processing

M. Letizia Corradini was born in Macerata, Italy, in 1961. She received the Doctor Degree in electronic engineering from the University of Ancona, Ancona, Italy, in 1987, where she is a Ph.D. student in Bioengineering. Her main interests are in the field of analysis and modelling of human motor behavior and in system identification.