A neurofuzzy inference system based on biomechanical features for the evaluation of the effects of physical training.
The current study aimed to evaluate physical training effects. For this purpose, a classifier was implemented by taking into account biomechanical features selected from force-plate measurements and a neurofuzzy algorithm for data management and relevant decision-making. Measurements included two sets of sit-to-stand (STS) trials involving two homogeneous groups, experimental and control, of elders. They were carried out before and after a 12-week heavy resistance strength-training program undergone by the experimental group. Pre- and post-training differences were analysed, and percentages of membership to "trained" and "untrained" fuzzy sets calculated. The method was shown to be appropriate for detecting significant training-related changes. Detection accuracy was higher than 87%. Slightly weaker results were obtained using a neural approach, suggesting the need for a larger sample size. In conclusion, the use of a set of biomechanical features and of a neurofuzzy algorithm allowed to propose a global score for evaluating the effectiveness of a specific training program.
Vannozzi
G
G
Department of Human Movement and Sport Sciences, Istituto Universitario di Scienze, Motorie, Rome, Italy. giuseppe.vannozzi@iusm.it
Pecoraro
F
F
Caserotti
P
P
Cappozzo
A
A
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001696:Biomechanical Phenomena / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D005081:Exercise Therapy; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008875:Middle Aged; D053580:Muscle Strength