A neurofuzzy inference system based on biomechanical features for the evaluation of the effects of physical training.

Abstract:

: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.

authors

Vannozzi G,Pecoraro F,Caserotti P,Cappozzo A

doi

10.1080/10255840701550915

subject

Has Abstract

pub_date

2008-02-01 00:00:00

pages

11-7

issue

1

eissn

1025-5842

issn

1476-8259

pii

783005808

journal_volume

11

pub_type

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