Handgrip strength evaluation using neuro fuzzy approach
Handgrip assessment is a useful method to monitor patient rehabilitation. The neurofuzzy analysis provides system identification and interpretability of fuzzy models and learning capability of neural networks. The purpose of this study is to collect handgrip strength of patients and distinguish them...
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| Format: | Article |
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2010
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| _version_ | 1825719134026989568 |
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| author | Seng, W.C. Chitsaz, M. |
| author_facet | Seng, W.C. Chitsaz, M. |
| author_sort | Seng, W.C. |
| collection | UM |
| description | Handgrip assessment is a useful method to monitor patient rehabilitation. The neurofuzzy analysis provides system identification and interpretability of fuzzy models and learning capability of neural networks. The purpose of this study is to collect handgrip strength of patients and distinguish them from the normal persons. Multilevel Perception neural network utilizes the back-propagation learning algorithm is suitable to discover relationships and patterns in the dataset. When the parameters are well tuned, the expert rules in the training data are captured and stored as expert weights of the neural network. The expert rules define the membership function for the fuzzy system. The fuzzy model based on the membership function, fed in by the neural network will intelligently classify the data. The results indicate that the classification accuracy of normal and pathological patients are 90 and 75 respectively. Moreover, this research demonstrates the feasibility of a novel handgrip design because the force measurements variance of the conventional LIDO machine and our designed handgrip is only 0.169. |
| first_indexed | 2024-03-06T05:15:01Z |
| format | Article |
| id | um.eprints-5700 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:15:01Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | um.eprints-57002013-04-17T02:33:24Z http://eprints.um.edu.my/5700/ Handgrip strength evaluation using neuro fuzzy approach Seng, W.C. Chitsaz, M. T Technology (General) Handgrip assessment is a useful method to monitor patient rehabilitation. The neurofuzzy analysis provides system identification and interpretability of fuzzy models and learning capability of neural networks. The purpose of this study is to collect handgrip strength of patients and distinguish them from the normal persons. Multilevel Perception neural network utilizes the back-propagation learning algorithm is suitable to discover relationships and patterns in the dataset. When the parameters are well tuned, the expert rules in the training data are captured and stored as expert weights of the neural network. The expert rules define the membership function for the fuzzy system. The fuzzy model based on the membership function, fed in by the neural network will intelligently classify the data. The results indicate that the classification accuracy of normal and pathological patients are 90 and 75 respectively. Moreover, this research demonstrates the feasibility of a novel handgrip design because the force measurements variance of the conventional LIDO machine and our designed handgrip is only 0.169. 2010 Article PeerReviewed Seng, W.C. and Chitsaz, M. (2010) Handgrip strength evaluation using neuro fuzzy approach. Malaysian Journal of Computer Science, 23 (3). p. 166. ISSN 0127-9084, http://ejum.fsktm.um.edu.my/article/975.pdf |
| spellingShingle | T Technology (General) Seng, W.C. Chitsaz, M. Handgrip strength evaluation using neuro fuzzy approach |
| title | Handgrip strength evaluation using neuro fuzzy approach |
| title_full | Handgrip strength evaluation using neuro fuzzy approach |
| title_fullStr | Handgrip strength evaluation using neuro fuzzy approach |
| title_full_unstemmed | Handgrip strength evaluation using neuro fuzzy approach |
| title_short | Handgrip strength evaluation using neuro fuzzy approach |
| title_sort | handgrip strength evaluation using neuro fuzzy approach |
| topic | T Technology (General) |
| work_keys_str_mv | AT sengwc handgripstrengthevaluationusingneurofuzzyapproach AT chitsazm handgripstrengthevaluationusingneurofuzzyapproach |