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.
journal_name
Comput Methods Biomech Biomed Enginauthors
Vannozzi G,Pecoraro F,Caserotti P,Cappozzo Adoi
10.1080/10255840701550915subject
Has Abstractpub_date
2008-02-01 00:00:00pages
11-7issue
1eissn
1025-5842issn
1476-8259pii
783005808journal_volume
11pub_type
杂志文章,随机对照试验abstract::The aim of this study was to evaluate the stress distributions and deformations of the temporomandibular joint (TMJ) during different periods before and after sagittal split ramus osteotomy (SSRO). A three-dimensional finite element model of the mandible and TMJ was established, based on the preoperative CT of a patie...
journal_title:Computer methods in biomechanics and biomedical engineering
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journal_title:Computer methods in biomechanics and biomedical engineering
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journal_title:Computer methods in biomechanics and biomedical engineering
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journal_title:Computer methods in biomechanics and biomedical engineering
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