A large number of people with a movement problem forms a relevant social and medical problem in all countries. The rapidly growing number of elderly people. who inevitably experience increasing limitations in their functioning as they grow older. is a cause of major international concern. Only in the European Community. 10% of the population is suffering from more or less severe motor problems. Awareness of disability costs and demographic developments have directed the poHcy of goverrunents to quality of life problems. More than in the past, research devoted to diseases of the neuro·musculoskeletal system is supported. This regards diagnosis. surgical and non-surgical treatment, rehabilitation and prevention. In all of these areas biomechanics is essential for the assessment of the mechanical functioning of healthy subjects and patients. Movement analysis is one of the most important parts of biomechanlcal research. Since the end of the 19th century there have been attempts to assess movement in an objective and quantitative manner (Muybridge, 1887; Marey, 1894; Braune & Fischer, 1895). During the past 20 yearsJ regular technological developments like microelectronics and fast computational tools have made this goal easier to achieve. Nowadays, in the field of Biomechanical Engineering more and more sophisticated systems for movement analysis(MA) have been developed. Significant results have been obtained, in several fields such as Rehabilitation, Ergonomics, Sport, Biomechanics and orthopedics. However, in rehabilitation, MA has received limited clinical acceptance, at least in Europe. In 1989, the European Conununity approved a project on Computer Aided Movement Analysis in a Rehabilitation Context (CAMARC). In general tenus, the purpose of the project was to render procedures and instruments for MA useful for patients and clinical doctors through suitable refmements of both instrumentation and software. In other terms, the overall objective of the CAMARC project was the transfer of the ever-improving bioengineering methodology and techniques for MA to the clinical environment. An important cause of the gap between the labora tory and the clinic could be the fact that stance and movement analysis procedures are generally aimed at the understanding of mechanisms at a rather basic levelJ whereas many clinical questions require an overall assessment of motor behavior in terms of skills instead of functions.

artificial intelligence, fuzzy logic, neural networks, recognition of patterns
E.S. Gelsema , C.J. Snijders (Chris)
Erasmus University Rotterdam
Stichting Anna-Fonds
hdl.handle.net/1765/18577
Erasmus MC: University Medical Center Rotterdam

Kiani, K. (1997, November 13). Recognition of patterns in multichannel recorded data using artificial neural networks and fuzzy rule based systems: application to daily life motor activities. Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/18577