Relationship between muscle activities and different movement patterns on an unstable platform using data mining

Authors:
Jung-Ja Kim;Yong-Jun Piao;Ah Reum Lee;Tae-Kyu Kwon;Yonggwan Won
Affiliations:
Division of Biomedical Engineering, College of Engineering, Chonbuk National University, Jeonju-si, Jeonbuk, Republic of Korea;Department of Biomedical Engineering, Graduate school, Chonbuk National University, Jeonju-si, Jeonbuk, Republic of Korea;Department of Biomedical Engineering, Graduate school, Chonbuk National University, Jeonju-si, Jeonbuk, Republic of Korea;Division of Biomedical Engineering, College of Engineering, Chonbuk National University, Jeonju-si, Jeonbuk, Republic of Korea;Department of Computer Engineering, Chonnam National University, Gwangju, Republic of Korea
Venue:
WSEAS Transactions on Information Science and Applications
Year:
2009

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Abstract

Association rule mining is widely used in the market-basket analysis. The association rule discovery can mine the rules that include more beneficial information by reflecting item importance for special products. Association rule mining could be a promising approach for clinical decision support system by discovering meaningful hidden rules and patterns from large volume of data obtained from the problem domain. The objective of this study was to analyze the muscle activities of different movement patterns on a training system for posture control using an unstable platform through association rule mining methodology. In this research, in order to find relational rules between posture training type and muscle activation pattern, we investigated an application of the association rule mining to the biomechanical data obtained mainly for evaluation of postural control ability. To investigate the relationship of the different movement patterns and muscle activities, fifteen healthy young subjects took part in a series of postural control training using a training system that we developed. The electromyography of the muscles in the lower limbs were recorded and analyzed under the different movement patterns. An improved association rule mining methodology was applied to analyze the relationship of the movement patterns and muscle activities. The results showed the significant differences in muscle activities for the different movement patterns. The experimental results suggested that, through the choice of different movement pattern, the training for lower extremity strength could be performed on specific muscles in different intensity. And, the ability of postural control could be improved by the training for lower extremity strength. Through the analysis results, we tried to find the best training method to improve the ability of postural control through improving the lower extremity muscular strength. The discovered rules could be used as a more useful knowledge for the rehabilitation and clinical expert's.