Generalization of ann model in classifying stance and swing phases of gait using emg signals
Exposure to physical therapy in rehabilitation shows a major interest in recent years. Even though the detection of gait events based on Electromyography (EMG) signals help the development of various assistive devices, the main issue arises on how to utilize EMG signals especially for two phases, st...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2019
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| Online Access: | http://eprints.utm.my/89245/1/MohdAziziRahman2019_GeneralizationofAnnModel.pdf |
| _version_ | 1796864790908370944 |
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| author | Nazmi, N. Abdul Rahman, M. A. Mohammed Ariff, M. H. Ahmad, S. A. |
| author_facet | Nazmi, N. Abdul Rahman, M. A. Mohammed Ariff, M. H. Ahmad, S. A. |
| author_sort | Nazmi, N. |
| collection | ePrints |
| description | Exposure to physical therapy in rehabilitation shows a major interest in recent years. Even though the detection of gait events based on Electromyography (EMG) signals help the development of various assistive devices, the main issue arises on how to utilize EMG signals especially for two phases, stance and swing. Previous works had proposed various classification model of EMG signals for five and seven phases. However, the performance of the classification model for any individual has not been explored. Thus, this study investigate the generalization of classification model for two gait phases, stance and swing based on EMG signals. The model was developed by extracting five time domain (TD) features and fed into a classifier, artificial neural network (ANN). Eight participants were divided into two groups that is learned data and unlearned data. The ANN model was designed based on learned data with levenberg maquardt (LM) training algorithm. Then, the model will be further evaluated with EMG signals of both unlearned data and learned data to observe the generalization of ANN model. The ANN model gained 87.4% of classification accuracy in discriminatiing stance phase and swing phase. This study found the generalization of the ANN model were acceptable with 87.5% for learned data and 77% for unlearned data. Future works could enhance the classification accuracy with different TD features and number of hidden neurons for ANN. |
| first_indexed | 2024-03-05T20:47:07Z |
| format | Conference or Workshop Item |
| id | utm.eprints-89245 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:47:07Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | utm.eprints-892452021-02-09T02:37:23Z http://eprints.utm.my/89245/ Generalization of ann model in classifying stance and swing phases of gait using emg signals Nazmi, N. Abdul Rahman, M. A. Mohammed Ariff, M. H. Ahmad, S. A. T Technology (General) Exposure to physical therapy in rehabilitation shows a major interest in recent years. Even though the detection of gait events based on Electromyography (EMG) signals help the development of various assistive devices, the main issue arises on how to utilize EMG signals especially for two phases, stance and swing. Previous works had proposed various classification model of EMG signals for five and seven phases. However, the performance of the classification model for any individual has not been explored. Thus, this study investigate the generalization of classification model for two gait phases, stance and swing based on EMG signals. The model was developed by extracting five time domain (TD) features and fed into a classifier, artificial neural network (ANN). Eight participants were divided into two groups that is learned data and unlearned data. The ANN model was designed based on learned data with levenberg maquardt (LM) training algorithm. Then, the model will be further evaluated with EMG signals of both unlearned data and learned data to observe the generalization of ANN model. The ANN model gained 87.4% of classification accuracy in discriminatiing stance phase and swing phase. This study found the generalization of the ANN model were acceptable with 87.5% for learned data and 77% for unlearned data. Future works could enhance the classification accuracy with different TD features and number of hidden neurons for ANN. 2019 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/89245/1/MohdAziziRahman2019_GeneralizationofAnnModel.pdf Nazmi, N. and Abdul Rahman, M. A. and Mohammed Ariff, M. H. and Ahmad, S. A. (2019) Generalization of ann model in classifying stance and swing phases of gait using emg signals. In: 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018, 3-6 Dec 2018, Borneo Convention Centre KuchingDemak-Isthmus Bridge, Jalan Keruing, SejingkatKuching; Malaysia. http://www.dx.doi.org/10.1109/IECBES.2018.8626626 |
| spellingShingle | T Technology (General) Nazmi, N. Abdul Rahman, M. A. Mohammed Ariff, M. H. Ahmad, S. A. Generalization of ann model in classifying stance and swing phases of gait using emg signals |
| title | Generalization of ann model in classifying stance and swing phases of gait using emg signals |
| title_full | Generalization of ann model in classifying stance and swing phases of gait using emg signals |
| title_fullStr | Generalization of ann model in classifying stance and swing phases of gait using emg signals |
| title_full_unstemmed | Generalization of ann model in classifying stance and swing phases of gait using emg signals |
| title_short | Generalization of ann model in classifying stance and swing phases of gait using emg signals |
| title_sort | generalization of ann model in classifying stance and swing phases of gait using emg signals |
| topic | T Technology (General) |
| url | http://eprints.utm.my/89245/1/MohdAziziRahman2019_GeneralizationofAnnModel.pdf |
| work_keys_str_mv | AT nazmin generalizationofannmodelinclassifyingstanceandswingphasesofgaitusingemgsignals AT abdulrahmanma generalizationofannmodelinclassifyingstanceandswingphasesofgaitusingemgsignals AT mohammedariffmh generalizationofannmodelinclassifyingstanceandswingphasesofgaitusingemgsignals AT ahmadsa generalizationofannmodelinclassifyingstanceandswingphasesofgaitusingemgsignals |