**2.3 The structure of the AMSS system**

The central element of the system is the CME-AMSS electronic central module, which has at least four groups of I/O ports, enabling data download between peripheral devices and program modules (1–5). The system is controlled by the PP-AMSS measuring software platform implemented in the "DELPHI 7.0 professional" environment, having (1) the BW-SSSP benchmark database; (2) the BWW-AMSS results database, available by patient ID; the SSI artificial intelligence system base-AMSS; as well as a database of tool modules, containing:


**31**

by the TA-AMSS test suite (**Figures 3–7**).

*Theoretical Biomechanics: Design of the Associated Measurement Symmetry System*

acquisition of specific lateralization indicators.

measurements should be made on a matrix of symmetry markers, enabling the

3.**The PTD-AMSS dynamic planimetric testing module** is part of the PP-AMSS software platform intended for the acquisition of measurement data from devices connected to the central CME-SSSP electronic device for extracorporeal measurement of body movements and its anomalies, in particular enabling parametric evaluation related to movement marker drift, recorded as

the sum of parallel asymmetric strings marked with a time marker.

4.**The MNA-AMSS analytical tool module** is part of the PP-SSSP software platform intended for the acquisition of measurement data from the CME-SSSP central electronic module connected via an analytical interface. It connects with devices for extracorporeal measurement of sample parameters, mainly body fluids and cells, as well as for measuring physicochemical parameters measured by applicators having direct contact with the examined tissues of the

5.**The MTB-AMSS biometric testing module** is part of the PP-AMSS software platform intended for the acquisition of measurement data from dynamic measurement devices connected to the central CME-AMSS electronic module that has a direct contact with the patient's tissues (e.g., plethysmography, pulse oximetry, EEG, ECG, EMG, magnetometry, resistometry and resistive tomography, accelerometry, gyroscopy, impulse perception threshold assessment).

6.**The module of artificial intelligence systems SSI-AMSS** allows, depending on needs, the use of advanced inference systems to perform (a) erosion of the information stream without loss of relevant content, (b) thematic organization of information, (c) diagnostic inference based on decision support systems (hypothesis generators), and (d) inferences based on expert systems and (e) determining the numerical dimension of disease progression and remission trends (under the influence of treatment). A large amount of collected data is connected with the problem of their effective processing, aimed at obtaining cross-sectional knowledge about the studied phenomenon. In medical diagnostics aided by computer methods of data analysis, making the right decision is extremely important because it is about time, life, and costs of patient treatment.

Broadly defined, clinical metrology parameterizes the natural (and test), structural, physicochemical, biomechanical, and behavioral characteristics of the patient in the state of health and disease, giving the possibility of assigning the above features to numerical indicators that facilitate the criteria for health status, specific disease entities, as well as stages of treatment (progression/regression) disease. In our opinion, one of the most important, yet useful, features that facilitate assessment of the clinical state is determining (on the quotient scale) multiparameter lateralization indices, i.e., symmetry disorders in marker points of the twin body structures and presenting the degree of their asymmetry on one, multiaxial graph, where OX axes are time stamp calibrated. According to the authors, this is the first real and moderately expensive method, enabling simultaneous, global multipath view of the patient with the clinical symptomatology of a specific disease syndrome, comparing the subjectively perceived image from the diagnostician with the parameterizing broadband graph of disease asymmetries. It is worth adding that the subjectivity of the patient with whom the interview is collected is partially verified

*DOI: http://dx.doi.org/10.5772/intechopen.92758*

treated patient.

measurements should be made on a matrix of symmetry markers, enabling the acquisition of specific lateralization indicators.


Broadly defined, clinical metrology parameterizes the natural (and test), structural, physicochemical, biomechanical, and behavioral characteristics of the patient in the state of health and disease, giving the possibility of assigning the above features to numerical indicators that facilitate the criteria for health status, specific disease entities, as well as stages of treatment (progression/regression) disease. In our opinion, one of the most important, yet useful, features that facilitate assessment of the clinical state is determining (on the quotient scale) multiparameter lateralization indices, i.e., symmetry disorders in marker points of the twin body structures and presenting the degree of their asymmetry on one, multiaxial graph, where OX axes are time stamp calibrated. According to the authors, this is the first real and moderately expensive method, enabling simultaneous, global multipath view of the patient with the clinical symptomatology of a specific disease syndrome, comparing the subjectively perceived image from the diagnostician with the parameterizing broadband graph of disease asymmetries. It is worth adding that the subjectivity of the patient with whom the interview is collected is partially verified by the TA-AMSS test suite (**Figures 3–7**).

*Recent Advances in Biomechanics*

stamp (T) (**Figure 2**)

3.The application of the numerical values of the above coefficients to form a measurement graph, i.e., a sequence of graphical and parametric symbols that reflect the state of the organism (S), for (n) parameters, recorded with a time

4.Application of the idea of a measuring graph to determine a standard profile graph, for neonatal [GPS (n)], children (2–8 years) [GPS (d)], adolescent (9–14 years) [GPS (m)], juvenile (15–20) [GPS (md)], adult (21–30) [GPS (d1)], adult (31–40) [GPS (d2)], adult (41–50) [GPS (d3)], adult (51–60) [GPS (d4)], involutionary (61–70) [GPS (i1)], involutionary (71–80) [GPS

(i2)], senile (81–90) [GPS (s1)], and old age (91–100) [GPS (s2)]

graph [GPS (s1–n)] for athletes in disciplines from 1 to n

[GPZ (n)], pilot [GPZ (p)], and astronaut [GPZ (ka)]

base-AMSS; as well as a database of tool modules, containing:

tion profile graph [GPD (p1–n)]

**2.3 The structure of the AMSS system**

given age range)

5.Application of the measurement graph idea to determine the sport profile

6.Application of the measuring graph idea to determine the professional profile graph, in particular for special occupations, e.g., commando [GPZ (ks)], diver

7.Application of the measurement graph idea to reflect the criteria of pathologies of well-verified and characteristic disease entities in order to obtain a dysfunc-

8.Determining the progression trend for the dysfunction profile graph (worsening of disease symptoms on the time axis) or regression trend (disappearance of disease symptoms until reaching the standard or sport profile graph in a

The central element of the system is the CME-AMSS electronic central module, which has at least four groups of I/O ports, enabling data download between peripheral devices and program modules (1–5). The system is controlled by the PP-AMSS measuring software platform implemented in the "DELPHI 7.0 professional" environment, having (1) the BW-SSSP benchmark database; (2) the BWW-AMSS results database, available by patient ID; the SSI artificial intelligence system

1.**TA-AMSS survey test module** is a collection of computer questionnaire tests, in particular psychological and quality of life scales, selected as useful for medical diagnostics, according to the algorithm for creating baseline assessment, assessment during and after treatment. In practice, intelligence tests (Stanford-Binet test, Raven matrices), personality and interests test (Mittenecker-Thoman) and language predispositions, depression tests (BDI,

2.**Planimetric static test module PTS-AMSS** is a computerized package of software tools for automatic planimetric measurements on static files of radiological, scintigraphic, ultrasound, and thermographic images (main location of 2D, 3D points, distance, angles). The use of these easy-to-use measuring tools, and through them the use of a lot of planimetric operations, gives the possibility of creating numerical equivalents of specific elements of the matrix of any image of biological structures. To improve the functionality of the system,

MADRS, HADS, Hamilton), and tree projection test were used.

**30**

#### **Figure 3.**

*Award for the idea and construction scheme of the AMSS at (a) the 7th International Congress of Polish Rehabilitation Society (Łódź 2010) and (b) the gold medal of the 26th Physical Medicine and Rehabilitation Fair in Łódź 2019, for the device - PSB1 posturometer to implement the assumptions from 2010.*

#### **Figure 4.**

*The main dialogue box of the AMSS system (a) SP-AMSS, connected to a computer system module (b) CS-AMSS, having the dialogue interface, image interface, biometric interface and analytical interface.*

## **2.4 The stream of measurement data**

The possibility of using increasingly sophisticated methods for recording parameters of subtle vital functions reveals an increasingly important issue of time interdependence of monitored parameters in clinical metrology. It is not of course of great importance when a single measurement of individual RR, HR, and temperature parameters is made. The possibility of multi-track data recording offered by rapidly developing metrological techniques meant that, together with the sensitivity and scope of monitoring and recording of parallel life functions, the relationship of their instantaneous values with time synchronization on the scale of the whole organism increases. The study of the temporal variability of

**33**

*Theoretical Biomechanics: Design of the Associated Measurement Symmetry System*

*The software platform SP-AMSS consists of (a) survey tests STM-AMSS (particular psychological and quality of life scales) cooperating with the dialogue interface and the image interface, (b) static planimetric tests MSPT-AMSS (package of utility programs for automatic planimetric measurements on files with static radiological, scintigraphic, ultrasound, and thermographic images) cooperating with the image interface.*

skin thermoemission, without synchronization with the assessment of blood flow in microcirculation and electrical heart action, may be helpful when the assessment is made by an experienced physician who subconsciously takes into account synchronization patterns. However, in the situation of more and more frequent use of intelligent techniques (e.g., neural networks) and mathematical inference rules in order to generate diagnostic hypotheses by diagnostic support systems, the level of correlation of input determinants depends on the level of correlation of partial determinants and, as a consequence, the reliability of diagnosis. In the introduction, the thesis of the synchronicity of the processes of regulation of homeostasis of the body was put forward. The rapid increase in patient data forces the need for intelligent techniques of data segregation and processing (e.g., rough sets), as well as systems supporting diagnostic decision (expert systems). It should be added, however, that one of the key criteria affecting the quality of

*The software platform SP-AMSS consists of (a) dynamic planimetric tests MDPT-AMSS for the parametric evaluation of movement-associated marker drift, cooperating with the image interface and (b) analytical tools ATM-AMSS (with analytical interface to communicate with devices for extracorporeal measurement of sample parameters, particularly body fluids and cells). Devices in (b): SDEE—separator for dynamic centrifugal and electromagnetic electrochromatography (PL 171643); SPERE—separator for separation of particles by electrophoresis in rotational electromagnetic gradient (PL187203); SPSPEREG—separator for particle separation by phonoinductive electrophoresis in rotational electromagnetic gradient (PL 186372); ECCC electromagnetic controlled column chromatography (PL 175577); MA—microwave analyzer (PL 194256); DLGA—dynamic liquid and gas analyzer (P416515); TMCCB—transdermal meter for the concentration of chemicals in the blood (W110552); RSDSBG—resonant stimulating and diagnostic system of bone growth (PL* 

*DOI: http://dx.doi.org/10.5772/intechopen.92758*

**Figure 5.**

**Figure 6.**

*203731); UA—urine analyzer [19, 20].*

*Theoretical Biomechanics: Design of the Associated Measurement Symmetry System DOI: http://dx.doi.org/10.5772/intechopen.92758*

#### **Figure 5.**

*Recent Advances in Biomechanics*

**32**

**Figure 3.**

**Figure 4.**

**2.4 The stream of measurement data**

The possibility of using increasingly sophisticated methods for recording parameters of subtle vital functions reveals an increasingly important issue of time interdependence of monitored parameters in clinical metrology. It is not of course of great importance when a single measurement of individual RR, HR, and temperature parameters is made. The possibility of multi-track data recording offered by rapidly developing metrological techniques meant that, together with the sensitivity and scope of monitoring and recording of parallel life functions, the relationship of their instantaneous values with time synchronization on the scale of the whole organism increases. The study of the temporal variability of

*The main dialogue box of the AMSS system (a) SP-AMSS, connected to a computer system module (b) CS-AMSS, having the dialogue interface, image interface, biometric interface and analytical interface.*

*Award for the idea and construction scheme of the AMSS at (a) the 7th International Congress of Polish Rehabilitation Society (Łódź 2010) and (b) the gold medal of the 26th Physical Medicine and Rehabilitation* 

*Fair in Łódź 2019, for the device - PSB1 posturometer to implement the assumptions from 2010.*

*The software platform SP-AMSS consists of (a) survey tests STM-AMSS (particular psychological and quality of life scales) cooperating with the dialogue interface and the image interface, (b) static planimetric tests MSPT-AMSS (package of utility programs for automatic planimetric measurements on files with static radiological, scintigraphic, ultrasound, and thermographic images) cooperating with the image interface.*

#### **Figure 6.**

*The software platform SP-AMSS consists of (a) dynamic planimetric tests MDPT-AMSS for the parametric evaluation of movement-associated marker drift, cooperating with the image interface and (b) analytical tools ATM-AMSS (with analytical interface to communicate with devices for extracorporeal measurement of sample parameters, particularly body fluids and cells). Devices in (b): SDEE—separator for dynamic centrifugal and electromagnetic electrochromatography (PL 171643); SPERE—separator for separation of particles by electrophoresis in rotational electromagnetic gradient (PL187203); SPSPEREG—separator for particle separation by phonoinductive electrophoresis in rotational electromagnetic gradient (PL 186372); ECCC electromagnetic controlled column chromatography (PL 175577); MA—microwave analyzer (PL 194256); DLGA—dynamic liquid and gas analyzer (P416515); TMCCB—transdermal meter for the concentration of chemicals in the blood (W110552); RSDSBG—resonant stimulating and diagnostic system of bone growth (PL 203731); UA—urine analyzer [19, 20].*

skin thermoemission, without synchronization with the assessment of blood flow in microcirculation and electrical heart action, may be helpful when the assessment is made by an experienced physician who subconsciously takes into account synchronization patterns. However, in the situation of more and more frequent use of intelligent techniques (e.g., neural networks) and mathematical inference rules in order to generate diagnostic hypotheses by diagnostic support systems, the level of correlation of input determinants depends on the level of correlation of partial determinants and, as a consequence, the reliability of diagnosis. In the introduction, the thesis of the synchronicity of the processes of regulation of homeostasis of the body was put forward. The rapid increase in patient data forces the need for intelligent techniques of data segregation and processing (e.g., rough sets), as well as systems supporting diagnostic decision (expert systems). It should be added, however, that one of the key criteria affecting the quality of

#### **Figure 7.**

*The software platform SP-AMSS consists of (a) contact testing module (CTM) for the parametric evaluation of symmetry of parameters of temperature, photoplethysmography, parameters of resistance, impedance, reactance, and microcirculation blood flow, cooperating with the biometric interface and (b) artificial intelligence system (AIS).*

the computer system is proper preparation of the input data. Therefore, there is a problem of creating a synchronous measurement and inference system, based on the experience of using the distributed measurement system, which allows the conversion of parameters and their presentation on a common time axis in order to outline interparametric correlations and assess their significance by applying system inference rules. One of the possible solutions to this problem is multipath measurement data acquisition, synchronized by a common time base. The network structure of the system can be easily adapted to the conditions in which patients are diagnosed in several offices, or a series of tests are carried out in different places. Each measurement module is able to attach a time stamp to the data record that is being sent, which makes it possible to determine many factors that depend on it, such as the need for repeated examination or the impact of rehabilitation on the patient's health. The time stamp is an important parameter that significantly affects not only the strength of connections between the test results carried out by individual modules but also the determination of the usefulness of the tests and their validity period. This in turn has a significant impact on economic planning and modification of procedures towards more effective solutions. One of the important elements affecting the results of the system is the proper preparation of the input data. For this purpose, elements of rough set theory will be used. Knowledge is understood here as the ability to classify objects of the studied reality, and its representation uses information systems of which special type is decision boards. One of the purposes of using rough sets is to generate a good, in a sense, universal set of decision rules—sentences of the form: "if trait and (1) = value (1) and (n) = value (n) is decision = value (d)." The creation of decision algorithms involves, among others, problems of knowledge reduction (searching for minimal sets of independent attributes, giving full classification of objects) and searching for dependencies between data. Detection of unnecessary attributes in the decision table will allow obtaining information on the basis of which, for example, you can change the attribute—increasing the time interval or rejecting the attribute. The use of appropriate classification algorithms will allow to reduce the number of objects or change the area of research, e.g., the location of the thermometer. The use of association discovery algorithms will allow, for example, to detect relationships between attributes and thus determine the degree of correlation between them. In the further part of the research program, this will also reveal the relationship between the effectiveness of treatment and the applied medical therapy [21].

**35**

*Theoretical Biomechanics: Design of the Associated Measurement Symmetry System*

**2.6 The role of unified criteria for metrological assessment of the** 

The system association of multidisciplinary packages of measurement files with patient's ID was important but only a first step in the creation of a modern, unified storage system. It should be noted that, this way, objective part of distributed information that so far has been stored on a paper medium was centered and accumulated on electronical medium in a form of numerical laboratory and electrophysiological parameters, which were referred to objective calibration patterns, source files of imaging tests, as well as numerous descriptive information (results of consultations with specialists, descriptions of imaging tests) that do not have its objective calibration pattern. The use of fairly diverse diagnostics in terms of, e.g., locomotor tests and tests of neurophysiological functions used in several wards, creates some communication problems in the field of communicating the assessment of patient's momentary state and even more of clinical results achieved with the use of different types of therapy. This is particularly important when moving a patient from ward to ward or leaving home with the intention of being re-admitted to further therapy at a later date. In the above situations, it is sometimes difficult to objectively determine the initial state and compare it after a period of stay at home or in another ward, and besides, the doctors of

**musculoskeletal system**

The sectoral computerization of a single workplace or even a single healthcare facility becomes a controversial improvement due to the lack of compatibility, and, as a consequence, isolation in the area of information media flows to the traditional part of the system. This creates the need for duplication of media and procedures. A full-size hospital network integrating all workstations in one operating system, which is also used in other facilities, is the only way to effectively use the working time of sector operators and the fast, internal, and external flow of user and feedback information. An important factor determining the effectiveness and efficiency of the system is the central storage of information about operational entities (patients) and the correct solution of the issue of hierarchy of access to read and write data on the part of employees. Cross-department and cross-center patient information flow during consultations, laboratory tests, admission, and discharge should be based on a protocol of operational procedures unified at least at the national level. The first step in this direction, and also a minimal option, is to create an optimal parametric patient graph and an administrative reporting base, whose data, depending on the hierarchy of access (authorization code), can be obtained in the form of a file at any point of the network. The optimal variant assumes the introduction of an interactive archiving procedure, in which a multi-domain measurement file (parametric graph) is created under the patient's ID, which is gradually supplemented during the stay in subsequent diagnostic laboratories, consultation rooms, and hospital wards. The system's layout of file edit fields means that each diagnostic information is entered sporadically from the alphanumeric keyboard and most often applied in digital form, extending the electronically available level of knowledge about the patient's condition. Therefore, information about the patient takes the form of a sequence of parametric graphs on the time axis, which is particularly important when preparing discharge documents, which until now have taken the most time in the practice of a hospital doctor, since their creation required duplication of time using the keyboard in the procedure of manual rewriting of test results from paper medium. When the hospital network has access to the system layout of edit fields and parametric tables of the patient's information file, it automatically enters their content into the discharge document, which reduces the time spent by the doctor on administrative activities by at least 80% [22].

*DOI: http://dx.doi.org/10.5772/intechopen.92758*

**2.5 Information structure in the system**

*Theoretical Biomechanics: Design of the Associated Measurement Symmetry System DOI: http://dx.doi.org/10.5772/intechopen.92758*

#### **2.5 Information structure in the system**

*Recent Advances in Biomechanics*

**Figure 7.**

*intelligence system (AIS).*

the computer system is proper preparation of the input data. Therefore, there is a problem of creating a synchronous measurement and inference system, based on the experience of using the distributed measurement system, which allows the conversion of parameters and their presentation on a common time axis in order to outline interparametric correlations and assess their significance by applying system inference rules. One of the possible solutions to this problem is multipath measurement data acquisition, synchronized by a common time base. The network structure of the system can be easily adapted to the conditions in which patients are diagnosed in several offices, or a series of tests are carried out in different places. Each measurement module is able to attach a time stamp to the data record that is being sent, which makes it possible to determine many factors that depend on it, such as the need for repeated examination or the impact of rehabilitation on the patient's health. The time stamp is an important parameter that significantly affects not only the strength of connections between the test results carried out by individual modules but also the determination of the usefulness of the tests and their validity period. This in turn has a significant impact on economic planning and modification of procedures towards more effective solutions. One of the important elements affecting the results of the system is the proper preparation of the input data. For this purpose, elements of rough set theory will be used. Knowledge is understood here as the ability to classify objects of the studied reality, and its representation uses information systems of which special type is decision boards. One of the purposes of using rough sets is to generate a good, in a sense, universal set of decision rules—sentences of the form: "if trait and (1) = value (1) and (n) = value (n) is decision = value (d)." The creation of decision algorithms involves, among others, problems of knowledge reduction (searching for minimal sets of independent attributes, giving full classification of objects) and searching for dependencies between data. Detection of unnecessary attributes in the decision table will allow obtaining information on the basis of which, for example, you can change the attribute—increasing the time interval or rejecting the attribute. The use of appropriate classification algorithms will allow to reduce the number of objects or change the area of research, e.g., the location of the thermometer. The use of association discovery algorithms will allow, for example, to detect relationships between attributes and thus determine the degree of correlation between them. In the further part of the research program, this will also reveal the relationship between the effectiveness of treatment and the applied

*The software platform SP-AMSS consists of (a) contact testing module (CTM) for the parametric evaluation of symmetry of parameters of temperature, photoplethysmography, parameters of resistance, impedance, reactance, and microcirculation blood flow, cooperating with the biometric interface and (b) artificial* 

**34**

medical therapy [21].

The sectoral computerization of a single workplace or even a single healthcare facility becomes a controversial improvement due to the lack of compatibility, and, as a consequence, isolation in the area of information media flows to the traditional part of the system. This creates the need for duplication of media and procedures. A full-size hospital network integrating all workstations in one operating system, which is also used in other facilities, is the only way to effectively use the working time of sector operators and the fast, internal, and external flow of user and feedback information. An important factor determining the effectiveness and efficiency of the system is the central storage of information about operational entities (patients) and the correct solution of the issue of hierarchy of access to read and write data on the part of employees. Cross-department and cross-center patient information flow during consultations, laboratory tests, admission, and discharge should be based on a protocol of operational procedures unified at least at the national level. The first step in this direction, and also a minimal option, is to create an optimal parametric patient graph and an administrative reporting base, whose data, depending on the hierarchy of access (authorization code), can be obtained in the form of a file at any point of the network. The optimal variant assumes the introduction of an interactive archiving procedure, in which a multi-domain measurement file (parametric graph) is created under the patient's ID, which is gradually supplemented during the stay in subsequent diagnostic laboratories, consultation rooms, and hospital wards. The system's layout of file edit fields means that each diagnostic information is entered sporadically from the alphanumeric keyboard and most often applied in digital form, extending the electronically available level of knowledge about the patient's condition. Therefore, information about the patient takes the form of a sequence of parametric graphs on the time axis, which is particularly important when preparing discharge documents, which until now have taken the most time in the practice of a hospital doctor, since their creation required duplication of time using the keyboard in the procedure of manual rewriting of test results from paper medium. When the hospital network has access to the system layout of edit fields and parametric tables of the patient's information file, it automatically enters their content into the discharge document, which reduces the time spent by the doctor on administrative activities by at least 80% [22].

#### **2.6 The role of unified criteria for metrological assessment of the musculoskeletal system**

The system association of multidisciplinary packages of measurement files with patient's ID was important but only a first step in the creation of a modern, unified storage system. It should be noted that, this way, objective part of distributed information that so far has been stored on a paper medium was centered and accumulated on electronical medium in a form of numerical laboratory and electrophysiological parameters, which were referred to objective calibration patterns, source files of imaging tests, as well as numerous descriptive information (results of consultations with specialists, descriptions of imaging tests) that do not have its objective calibration pattern. The use of fairly diverse diagnostics in terms of, e.g., locomotor tests and tests of neurophysiological functions used in several wards, creates some communication problems in the field of communicating the assessment of patient's momentary state and even more of clinical results achieved with the use of different types of therapy. This is particularly important when moving a patient from ward to ward or leaving home with the intention of being re-admitted to further therapy at a later date. In the above situations, it is sometimes difficult to objectively determine the initial state and compare it after a period of stay at home or in another ward, and besides, the doctors of subsequent wards are forced to duplicate many initial descriptive activities. In this situation, instrumental and thus quantitative determination of the topography of receptor disorders of the skin, muscle strength, thoracic respiratory path, vector indicators in the Romberg test, California gait indicators of the patient's gait, muscle electromyographic parameters, or nerve conduction, according to the hospital standard, gives the objective component of the state temporary patient, to whom the clinical comment of the doctor is attached. The file is available at any point in the computer network [23].

#### **2.7 Hospital information system**

The main goal of the hospital information system (HIS) [22] should be to improve the quality and streamline the patient's treatment process and reduce the operating costs of a hospital. The first SSIs implemented were based on large workstations (mainframes) with uniform software. Currently, the concept of architecture based on network structure and modular software, not necessarily from one supplier, prevails. The network structure, emerging databases, and connection with other hospitals and scientific institutions result in an increase in the quality of medical services provided. The use of computers with a properly designed information system contributes to the elimination of a significant part of errors and mistakes, completes and unifies the data, facilitates faster access to data, allows more efficient use of equipment, and shortens the patient's waiting time for admission to the appropriate department and effective treatment. The exchange of experiences (in particular those described parametrically) between medical personnel of various departments and environments allows for the development of standards in routine clinical management and in unusual situations. The reference structure of the hospital information system should include:

*A system of* **data presentation** enabling the standardization of patient data and, consequently, sorting, filtering, and classification (e.g., for the purposes of statistics and epidemiology). An important feature is data availability 24 h a day, 7 days a week from any terminal of the network. This postulated computer patient card should contain (in addition to a record of personal information) information about immunizations, allergies to medications, laboratory and imaging tests, opinions of specialists, a list of possible problems and complications, a plan of visits and consultations, issued medical leaves, information on surgeries, prescription medicines, and others. This list can of course be modified depending on the unit. The main elements are as follows: (1) **Knowledge**—a connection through the Internet with other centers. The introduction of the telemedicine techniques—video consultations, transmissions showing the course of innovative surgical procedures and access to the resource databases (MEDLINE), disease codes and procedures, and literature—facilitates the process of diagnosis and treatment. It is necessary here to implement appropriate mechanisms and tools for defining queries and search the available resources. (2) **Communication**—cheap, quick, and easy medium for the physicians to communicate with each other or with patients. Great opportunities are given by the e-mail: e-mail, group mailing lists, SMS technology in mobile phones, videoconferencing, and video consultations. (3) **Alarms, reminders, and suggestions**—automatically generated messages when selected parameters of patient care do not meet the standards. It is a kind of a quality control system, difficult to achieve with other methods. The final decision to take a given action rests entirely with a licensed physician.

#### **2.8 The network structure**

At the moment, only systems using Web technologies are being considered [22] as the best solution from an economic and ergonomic point of view, also taking into

**37**

*Theoretical Biomechanics: Design of the Associated Measurement Symmetry System*

account the rapid development of the Internet and of related, so-called e-services. Already well mastered LAN and WAN technologies may be used for this purpose. Implemented software must be running in terminal emulation mode or in application mode client–server. A correct and strict control of access to the information is indispensable—mechanisms for logging into the system, password protection, supervised connections (scripting), as well as coding and encryption procedure of data meant to be transmitted. It is important due to the Personal Data Protection Act—L.G. No. 133, item. 833 of 1997. It is also important to ensure compatibility with various hardware platforms and operating systems (Windows 9x, Windows NT, Macintosh, UNIX). Thus, we need to provide multiple ways of presentation that provide for the most popular types of terminals: IBM3270, VT100, MS Windows, X-Windows. Another essential element in the architecture of the proposed information system must be taking into account various network protocols for the transmission and exchange of data, TCP/IP, IPX/SPX, and HL-7, as well as implementing special dictionary translator interface, responsible for the correct interpretation of information passing between the applications of the system. A conversion of data and codes of different applications is usually needed to create a common representation. Usually, such conversion is carried out "on the fly," using appropriate interfaces. To conclude, we also need to take care of a friendly and easy-to-use user interface.

Information theory (by Shannon) [22] refers to a sequence of events, to the occurrence of which a certain probability can be attributed. Such an approach means that both the amount of information that implies an event and the total amount of information in a particular series of events can be specified. Information is, therefore, the opposite of probability. Repeating information in the transmission (redundancy) reduces the transmission efficiency of the communication system (doubling the sequence) but increases immunity for disruptions in the information

The decision support system is an organized set of experts, procedures, and technical equipment used to support the effectiveness of the decision-making process. Its task is to improve the quality and efficiency of the decision-making process by simultaneous access to knowledge with a structured access path, as well as to the data with no such structure. Thus, the decision support system is a way to make two kinds of system resources available to the user: the source data bank and the bank of operational data processing methods. The computer decision support system is not able to replace human in any part of his activity; thus, it functions as (1) an instrument supporting the memory and tool-making capabilities of the decision-maker; (2) an incentive (or even a push factor) for precision and consistency in formulating research questions, method of demanding, and searching for the scope and criteria for making optimization; and (3) a facilitator for groups of decision-makers (in particular) operating at lower levels of the management structure, allowing to make informed decisions without the need to have confidential knowledge about the whole system. The development of decision support systems is carried out in the following directions: (1) interdisciplinary modernization of system methods by employing specialists not only in narrow specialties but above all in theory of decision-making, management, operation research, cognitive psychology, fuzzy logic, and mathematical modeling; (2) the broadly defined typing of information technology, the development of which moves toward electronic data collection and conversion of existing archives; and (3) development of technology and methods of communication with the electronic archives. Features of the decision support systems (DSS) include (1) presenting information in the form that is known to a

*DOI: http://dx.doi.org/10.5772/intechopen.92758*

**2.9 The elements of artificial intelligence**

canal (loss compensation).

#### *Theoretical Biomechanics: Design of the Associated Measurement Symmetry System DOI: http://dx.doi.org/10.5772/intechopen.92758*

account the rapid development of the Internet and of related, so-called e-services. Already well mastered LAN and WAN technologies may be used for this purpose. Implemented software must be running in terminal emulation mode or in application mode client–server. A correct and strict control of access to the information is indispensable—mechanisms for logging into the system, password protection, supervised connections (scripting), as well as coding and encryption procedure of data meant to be transmitted. It is important due to the Personal Data Protection Act—L.G. No. 133, item. 833 of 1997. It is also important to ensure compatibility with various hardware platforms and operating systems (Windows 9x, Windows NT, Macintosh, UNIX). Thus, we need to provide multiple ways of presentation that provide for the most popular types of terminals: IBM3270, VT100, MS Windows, X-Windows. Another essential element in the architecture of the proposed information system must be taking into account various network protocols for the transmission and exchange of data, TCP/IP, IPX/SPX, and HL-7, as well as implementing special dictionary translator interface, responsible for the correct interpretation of information passing between the applications of the system. A conversion of data and codes of different applications is usually needed to create a common representation. Usually, such conversion is carried out "on the fly," using appropriate interfaces. To conclude, we also need to take care of a friendly and easy-to-use user interface.

## **2.9 The elements of artificial intelligence**

Information theory (by Shannon) [22] refers to a sequence of events, to the occurrence of which a certain probability can be attributed. Such an approach means that both the amount of information that implies an event and the total amount of information in a particular series of events can be specified. Information is, therefore, the opposite of probability. Repeating information in the transmission (redundancy) reduces the transmission efficiency of the communication system (doubling the sequence) but increases immunity for disruptions in the information canal (loss compensation).

The decision support system is an organized set of experts, procedures, and technical equipment used to support the effectiveness of the decision-making process. Its task is to improve the quality and efficiency of the decision-making process by simultaneous access to knowledge with a structured access path, as well as to the data with no such structure. Thus, the decision support system is a way to make two kinds of system resources available to the user: the source data bank and the bank of operational data processing methods. The computer decision support system is not able to replace human in any part of his activity; thus, it functions as (1) an instrument supporting the memory and tool-making capabilities of the decision-maker; (2) an incentive (or even a push factor) for precision and consistency in formulating research questions, method of demanding, and searching for the scope and criteria for making optimization; and (3) a facilitator for groups of decision-makers (in particular) operating at lower levels of the management structure, allowing to make informed decisions without the need to have confidential knowledge about the whole system. The development of decision support systems is carried out in the following directions: (1) interdisciplinary modernization of system methods by employing specialists not only in narrow specialties but above all in theory of decision-making, management, operation research, cognitive psychology, fuzzy logic, and mathematical modeling; (2) the broadly defined typing of information technology, the development of which moves toward electronic data collection and conversion of existing archives; and (3) development of technology and methods of communication with the electronic archives. Features of the decision support systems (DSS) include (1) presenting information in the form that is known to a

*Recent Advances in Biomechanics*

**2.7 Hospital information system**

the hospital information system should include:

subsequent wards are forced to duplicate many initial descriptive activities. In this situation, instrumental and thus quantitative determination of the topography of receptor disorders of the skin, muscle strength, thoracic respiratory path, vector indicators in the Romberg test, California gait indicators of the patient's gait, muscle electromyographic parameters, or nerve conduction, according to the hospital standard, gives the objective component of the state temporary patient, to whom the clinical comment of the doctor is attached. The file is available at any point in the computer network [23].

The main goal of the hospital information system (HIS) [22] should be to improve the quality and streamline the patient's treatment process and reduce the operating costs of a hospital. The first SSIs implemented were based on large workstations (mainframes) with uniform software. Currently, the concept of architecture based on network structure and modular software, not necessarily from one supplier, prevails. The network structure, emerging databases, and connection with other hospitals and scientific institutions result in an increase in the quality of medical services provided. The use of computers with a properly designed information system contributes to the elimination of a significant part of errors and mistakes, completes and unifies the data, facilitates faster access to data, allows more efficient use of equipment, and shortens the patient's waiting time for admission to the appropriate department and effective treatment. The exchange of experiences (in particular those described parametrically) between medical personnel of various departments and environments allows for the development of standards in routine clinical management and in unusual situations. The reference structure of

*A system of* **data presentation** enabling the standardization of patient data and, consequently, sorting, filtering, and classification (e.g., for the purposes of statistics and epidemiology). An important feature is data availability 24 h a day, 7 days a week from any terminal of the network. This postulated computer patient card should contain (in addition to a record of personal information) information about immunizations, allergies to medications, laboratory and imaging tests, opinions of specialists, a list of possible problems and complications, a plan of visits and consultations, issued medical leaves, information on surgeries, prescription medicines, and others. This list can of course be modified depending on the unit. The main elements are as follows: (1) **Knowledge**—a connection through the Internet with other centers. The introduction of the telemedicine techniques—video consultations, transmissions showing the course of innovative surgical procedures and access to the resource databases (MEDLINE), disease codes and procedures, and literature—facilitates the process of diagnosis and treatment. It is necessary here to implement appropriate mechanisms and tools for defining queries and search the available resources. (2) **Communication**—cheap, quick, and easy medium for the physicians to communicate with each other or with patients. Great opportunities are given by the e-mail: e-mail, group mailing lists, SMS technology in mobile phones, videoconferencing, and video consultations. (3) **Alarms, reminders, and suggestions**—automatically generated messages when selected parameters of patient care do not meet the standards. It is a kind of a quality control system, difficult to achieve with other methods. The final decision to take a given action rests entirely with a licensed physician.

At the moment, only systems using Web technologies are being considered [22] as the best solution from an economic and ergonomic point of view, also taking into

**36**

**2.8 The network structure**

group of users, (2) selectivity of the information provided, (3) supporting and not replacing the decision-making process of the decision-maker, (4) supporting the structuring of the decision-making process, (5) flexibility and quick response to the user needs, (6) supporting various styles of decision-making, (7) acting in different phases of the decision-making process, and (8) cross-checking of various phases of intertwining decision-making processes realized according to different styles.

An expert system is a technically implemented or software-independent algorithm modeled on the activity of the nervous system that can, on the basis of provided or measurement data, make decisions regarding regulation of the control object in a specified range of prediction. It is the use of computer that solves complex problems requiring expert knowledge. To achieve this objective, the expert system simulates the process of human reasoning, using specific scientific knowledge of nature expressed with mathematical inference rules. An expert system (according to Feigenbaum) is "an intelligent computer program that uses inference procedures for solving problems, which are difficult enough to require significant expertise of the specialists. Knowledge, together with the rules of inference, can be regarded as a model of expertise normally achievable only by top professionals." The following types of DSS are distinguished: (1) **Specialist**—bespoke for specific users, regarding their specific problems and ways of solving them. (2) **System generators**—programs allowing for construction of specialized SWD, quickly and easily, based on the system core, often with no need to consult with industry authorities. (3) **Tools**—software supporting other systems, often added as modules or separate programs (graphics system, database type and method of access, software packages, etc.). Within DSS, there are categories that can be distinguished: (1) **Consulting systems**—presenting solutions to the user that can assess their quality. They are usually collected in order of increasing probability. (2) **Systems making decisions without human control**—they are used for the control of dangerous or microautomatic systems (e.g., insulin pumps). (3) **Criticizing systems**—they verify the problem presented to them in the form of a graph, equation, or diagram, showing a list of discrepancies (with known rules) and a list of suggested corrections. The result of an expert system may be the following: (1) **Diagnosis**—assessment of the current state of the object, based on the data processed on the basis of the installed inference rules. (2) **Prognosis**—prediction of future states of the data processed on the basis of the installed inference rules. (3) **Plan**—a sequential-structural description of the state to which the system aims at in a given operating cycle.

**Expert systems (ES)** are an evolutionarily younger product of the development of knowledge engineering, often based on the existing knowledge support systems. Both systems operate with the use of (1) artificial intelligence, (2) design methods, and (3) functional fusion of systems. The backbone of artificial intelligence can be used to build both DSS and ES. The main directions of the application are (1) model knowledge bases in the range representation codes especially for specific disease entities, access paths, generators of convergent, and alternative and contradictory definitions and (2) construction of sophisticated models of the simplest access and data presentation interfaces, with a tendency to engage in dialog in natural language (according to the principle of "the advanced background technology") [23].

In the large system of spatial regulation of the whole organism, there is a hierarchical-parallel organization of the interdependence of individual centers and circuits. It consists in the fact that the regulatory influence of parent structures on a number of processes at the lowest level is limited in the possibilities of changing local parameters only to the optimal values, which fall within the range of autonomous function of the subsystems. Principles of intersystemic cooperation shaped this way cause, that in moments of special mobilization of the entire body, it is not at the expense one organ or group of cells only, but each of the systems contributes

**39**

*Theoretical Biomechanics: Design of the Associated Measurement Symmetry System*

to homeostasis with its "regulatory surplus" that exists around its optimum of functioning. The disease process is based on local or generalized overrun beyond this principle. Creating models simulating the functions of living organisms should not be mere mind gymnastics or a test of skills in practical application of the possibilities of mathematics, electronics, and software, but a practical and functional facility solving specific problems in various fields of science. It is necessary to realize that in modern science, there is a number of excellent diagnostic methods used at the most basic level, without mathematical apparatus that guarantees high specificity and accuracy. A key task for an IT specialist in biology and medicine is the use of digital machines for the processing and description of signals or images of biological objects. Combining the power of the mathematical apparatus with an instant access to data library containing expert knowledge, a significant deepening of the level of problem understanding is obtained, even at the stage of preliminary data analysis. In this new situation, a doctor or biologist will have knowledge, normally reserved for highly specialized centers, and the practical consequences of

However, in order to use digital computational technique in humans or other living organisms, there is a need to "translate" the axial physiological functions of the body, both in the state of health and sickness, into the strict language of structural algorithms that can be identified with the "disease models," compiled then with higher-order language to the level of digital functions. The most valuable feature of the artificial neural networks is their ability to process information in a parallel way, as well as the learning process, that replaces traditional programming. The operation program of the network is included in the topology of connections and their parameters, that is to say weights. Solutions to the tasks the network should solve are presented by means of the output signals. The most important problem during the construction of an artificial neural network is the selection of the network architecture to a particular problem. The choice of a neuron model and the method of teaching the network are also important. The best solution for the classification tasks is one-way network, while, for example, for complex optimization structures, a feedback network. Learning is a process that leads to consolidation of specific behaviors, based on experience. From the technical point of view, the problem boils down to determining the product of the scalar signal vector and the instantaneous weight vector of a discrete network element that can operate in a time or space pattern. In the artificial neural networks, learning is a process where each stage is usually visible as a cause-effect relationship. Teaching the network means forcing specific response to the input signals given. In artificial neural network applications, supervised learning and indirect supervision learning can be distinguished. Teacher-assisted training of the network requires preparing an appropriate set of input and output data, i.e., the training set. During learning, each cycle checks whether the network response is as expected—if not, then the weight change process takes place. Teaching lasts until all responses to the learning set data are correct. They are considered to be such, when the difference between the output of the network and the expected signal is less than the established maximum error.

*DOI: http://dx.doi.org/10.5772/intechopen.92758*

this fact should not be long to wait for.

**2.10 Clinical implementations of AMSS system elements**

I introduced lateral coefficient methodology in medical diagnostics in 2000, initially to the description of the Lasegue test [24], later to assess goniometric techniques, X-ray planimetry, and podoscope planimetry [25]. At the turn of 2003–2005, the idea of the lateralization indicators developed in the direction of multipath symmetries [15]. Since that time, the idea of multipath symmetries was successfully tested clinically on medium groups of patients [26, 27] and large groups

#### *Theoretical Biomechanics: Design of the Associated Measurement Symmetry System DOI: http://dx.doi.org/10.5772/intechopen.92758*

to homeostasis with its "regulatory surplus" that exists around its optimum of functioning. The disease process is based on local or generalized overrun beyond this principle. Creating models simulating the functions of living organisms should not be mere mind gymnastics or a test of skills in practical application of the possibilities of mathematics, electronics, and software, but a practical and functional facility solving specific problems in various fields of science. It is necessary to realize that in modern science, there is a number of excellent diagnostic methods used at the most basic level, without mathematical apparatus that guarantees high specificity and accuracy. A key task for an IT specialist in biology and medicine is the use of digital machines for the processing and description of signals or images of biological objects. Combining the power of the mathematical apparatus with an instant access to data library containing expert knowledge, a significant deepening of the level of problem understanding is obtained, even at the stage of preliminary data analysis. In this new situation, a doctor or biologist will have knowledge, normally reserved for highly specialized centers, and the practical consequences of this fact should not be long to wait for.

However, in order to use digital computational technique in humans or other living organisms, there is a need to "translate" the axial physiological functions of the body, both in the state of health and sickness, into the strict language of structural algorithms that can be identified with the "disease models," compiled then with higher-order language to the level of digital functions. The most valuable feature of the artificial neural networks is their ability to process information in a parallel way, as well as the learning process, that replaces traditional programming. The operation program of the network is included in the topology of connections and their parameters, that is to say weights. Solutions to the tasks the network should solve are presented by means of the output signals. The most important problem during the construction of an artificial neural network is the selection of the network architecture to a particular problem. The choice of a neuron model and the method of teaching the network are also important. The best solution for the classification tasks is one-way network, while, for example, for complex optimization structures, a feedback network. Learning is a process that leads to consolidation of specific behaviors, based on experience. From the technical point of view, the problem boils down to determining the product of the scalar signal vector and the instantaneous weight vector of a discrete network element that can operate in a time or space pattern. In the artificial neural networks, learning is a process where each stage is usually visible as a cause-effect relationship. Teaching the network means forcing specific response to the input signals given. In artificial neural network applications, supervised learning and indirect supervision learning can be distinguished. Teacher-assisted training of the network requires preparing an appropriate set of input and output data, i.e., the training set. During learning, each cycle checks whether the network response is as expected—if not, then the weight change process takes place. Teaching lasts until all responses to the learning set data are correct. They are considered to be such, when the difference between the output of the network and the expected signal is less than the established maximum error.

### **2.10 Clinical implementations of AMSS system elements**

I introduced lateral coefficient methodology in medical diagnostics in 2000, initially to the description of the Lasegue test [24], later to assess goniometric techniques, X-ray planimetry, and podoscope planimetry [25]. At the turn of 2003–2005, the idea of the lateralization indicators developed in the direction of multipath symmetries [15]. Since that time, the idea of multipath symmetries was successfully tested clinically on medium groups of patients [26, 27] and large groups

*Recent Advances in Biomechanics*

group of users, (2) selectivity of the information provided, (3) supporting and not replacing the decision-making process of the decision-maker, (4) supporting the structuring of the decision-making process, (5) flexibility and quick response to the user needs, (6) supporting various styles of decision-making, (7) acting in different phases of the decision-making process, and (8) cross-checking of various phases of intertwining decision-making processes realized according to different styles. An expert system is a technically implemented or software-independent algorithm modeled on the activity of the nervous system that can, on the basis of provided or measurement data, make decisions regarding regulation of the control object in a specified range of prediction. It is the use of computer that solves complex problems requiring expert knowledge. To achieve this objective, the expert system simulates the process of human reasoning, using specific scientific knowledge of nature expressed with mathematical inference rules. An expert system (according to Feigenbaum) is "an intelligent computer program that uses inference procedures for solving problems, which are difficult enough to require significant expertise of the specialists. Knowledge, together with the rules of inference, can be regarded as a model of expertise normally achievable only by top professionals." The following types of DSS are distinguished: (1) **Specialist**—bespoke for specific users, regarding their specific problems and ways of solving them. (2) **System generators**—programs allowing for construction of specialized SWD, quickly and easily, based on the system core, often with no need to consult with industry authorities. (3) **Tools**—software supporting other systems, often added as modules or separate programs (graphics system, database type and method of access, software packages, etc.). Within DSS, there are categories that can be distinguished: (1) **Consulting systems**—presenting solutions to the user that can assess their quality. They are usually collected in order of increasing probability. (2) **Systems making decisions without human control**—they are used for the control of dangerous or microautomatic systems (e.g., insulin pumps). (3) **Criticizing systems**—they verify the problem presented to them in the form of a graph, equation, or diagram, showing a list of discrepancies (with known rules) and a list of suggested corrections. The result of an expert system may be the following: (1) **Diagnosis**—assessment of the current state of the object, based on the data processed on the basis of the installed inference rules. (2) **Prognosis**—prediction of future states of the data processed on the basis of the installed inference rules. (3) **Plan**—a sequential-structural descrip-

tion of the state to which the system aims at in a given operating cycle.

(according to the principle of "the advanced background technology") [23]. In the large system of spatial regulation of the whole organism, there is a hierarchical-parallel organization of the interdependence of individual centers and circuits. It consists in the fact that the regulatory influence of parent structures on a number of processes at the lowest level is limited in the possibilities of changing local parameters only to the optimal values, which fall within the range of autonomous function of the subsystems. Principles of intersystemic cooperation shaped this way cause, that in moments of special mobilization of the entire body, it is not at the expense one organ or group of cells only, but each of the systems contributes

**Expert systems (ES)** are an evolutionarily younger product of the development of knowledge engineering, often based on the existing knowledge support systems. Both systems operate with the use of (1) artificial intelligence, (2) design methods, and (3) functional fusion of systems. The backbone of artificial intelligence can be used to build both DSS and ES. The main directions of the application are (1) model knowledge bases in the range representation codes especially for specific disease entities, access paths, generators of convergent, and alternative and contradictory definitions and (2) construction of sophisticated models of the simplest access and data presentation interfaces, with a tendency to engage in dialog in natural language

**38**

of patients [6]. A very important consequence of this completely new approach to the diagnosis was making an important discovery and acquiring clinical evidence that together with asymmetric scheme of long-term physical adaptation of the organism in the course of pain syndromes, bone remodeling occurs in the twin supporting elements of the bone structure, which can be described using lateralization indicators [28].

A summary of the above idea was presented in 2010, in Łódź, at the 7th International Congress of the Polish Rehabilitation Society, in the form of paper entitled "A system of associated measurement symmetries as an EBM tool in standardizing the diagnosis and surveillance of the musculoskeletal system treatment," and then awarded by the Scientific Committee of the Congress. This paper contains the idea and the general modular design of associated measurement symmetry system (AMSS). This issue is discussed in more detail in two chapters of the postconference monograph [29–31]*.* Techniques of describing patient's clinical condition (taken from the SSSP system) were also used in a large clinical study, published as a book chapter in the United States [1].
