Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh
This research presents the development of quadriceps muscle models using several Artificial Intelligent techniques. The data of paraplegic muscle behavior consists of frequency, sampling time, pulse width and muscle torque have been obtained from Hospital Sungai Buloh. The data was collected from 20...
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| Format: | Thesis |
| Language: | English |
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2015
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| Online Access: | https://ir.uitm.edu.my/id/eprint/15276/ |
| _version_ | 1848803702042787840 |
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| author | Mohd Salleh, Syafinas |
| author_facet | Mohd Salleh, Syafinas |
| author_sort | Mohd Salleh, Syafinas |
| building | UiTM Institutional Repository |
| collection | Online Access |
| description | This research presents the development of quadriceps muscle models using several Artificial Intelligent techniques. The data of paraplegic muscle behavior consists of frequency, sampling time, pulse width and muscle torque have been obtained from Hospital Sungai Buloh. The data was collected from 20 paraplegic patient’s age between 40-60 years olds throughout the study. 722 data obtained from the data collection to be used for muscle model development. In this study Artificial Neural Network (ANN) will be used to develop paraplegic muscle models. There are Levenberg Marquardt (LM),Resilient Backpropagation (RP) and Scale Conjugate Gradient (SCG). Other AI techniques involve in this study are Fuzzy Logic and Adaptive Neural Fuzzy Interference System (ANFIS). The last technique that has been selected for the muscle model is Extended Kalman Filter (EKF) techniques. From the result, LM gives the best performance compare to others model developed. From analysis for all model develops, LM gives the lowest Mean Squared Error (MSE) 0.2007 followed by RP with MSE 0.2644, SCG 0.3758, Fuzzy Logic 0.7557, ANFIS 1.36 and EKF 29.68. In this analysis,the proposed EKF shows unreliable results where it produces the highest error compared to the other techniques. It shows that EKF is not suitable to be used for develop quadriceps muscle model with unsynchronized type of data behavior. The result obtained in this study can be used in the future for design and evaluation of various control strategies that could help avoid injuries during the experiment on real paraplegic patients |
| first_indexed | 2025-11-14T21:43:26Z |
| format | Thesis |
| id | uitm-15276 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:43:26Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-152762022-03-09T07:42:29Z https://ir.uitm.edu.my/id/eprint/15276/ Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh Mohd Salleh, Syafinas Artificial immune systems. Immunocomputers This research presents the development of quadriceps muscle models using several Artificial Intelligent techniques. The data of paraplegic muscle behavior consists of frequency, sampling time, pulse width and muscle torque have been obtained from Hospital Sungai Buloh. The data was collected from 20 paraplegic patient’s age between 40-60 years olds throughout the study. 722 data obtained from the data collection to be used for muscle model development. In this study Artificial Neural Network (ANN) will be used to develop paraplegic muscle models. There are Levenberg Marquardt (LM),Resilient Backpropagation (RP) and Scale Conjugate Gradient (SCG). Other AI techniques involve in this study are Fuzzy Logic and Adaptive Neural Fuzzy Interference System (ANFIS). The last technique that has been selected for the muscle model is Extended Kalman Filter (EKF) techniques. From the result, LM gives the best performance compare to others model developed. From analysis for all model develops, LM gives the lowest Mean Squared Error (MSE) 0.2007 followed by RP with MSE 0.2644, SCG 0.3758, Fuzzy Logic 0.7557, ANFIS 1.36 and EKF 29.68. In this analysis,the proposed EKF shows unreliable results where it produces the highest error compared to the other techniques. It shows that EKF is not suitable to be used for develop quadriceps muscle model with unsynchronized type of data behavior. The result obtained in this study can be used in the future for design and evaluation of various control strategies that could help avoid injuries during the experiment on real paraplegic patients 2015 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/15276/1/TM_SYAFINAS%20MOHD%20SALLEH%20EE%2015_5.pdf Mohd Salleh, Syafinas (2015) Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh. (2015) Masters thesis, thesis, Universiti Teknologi MARA. <http://terminalib.uitm.edu.my/15276.pdf> |
| spellingShingle | Artificial immune systems. Immunocomputers Mohd Salleh, Syafinas Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh |
| title | Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh |
| title_full | Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh |
| title_fullStr | Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh |
| title_full_unstemmed | Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh |
| title_short | Development of paraplegic quadriceps muscle models using artificial intelligent techniques / Syafinas Mohd Salleh |
| title_sort | development of paraplegic quadriceps muscle models using artificial intelligent techniques / syafinas mohd salleh |
| topic | Artificial immune systems. Immunocomputers |
| url | https://ir.uitm.edu.my/id/eprint/15276/ |