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.

Keywords:

• Biomechanics
• Neurofuzzy system
• Clustering
• Classification
• Physical training
• Elderly

Acknowledgements

Support and technical feedback from Prof. Lis Puggaard (ACES) and Dr. Claudia Mazzà (IUSM) are kindly acknowledged.