A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles
Background: In this study, three detecting approaches have been proposed and evaluated for online detection of balance situations during quiet standing. The applied methods were based on electromyography of the gastrocnemius muscles adopting the hidden Markov models. Methods: The levels of postural...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Shahid Beheshti University of Medical Sciences
2022-01-01
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| Series: | International Clinical Neuroscience Journal |
| Subjects: | |
| Online Access: | https://journals.sbmu.ac.ir/neuroscience/article/view/36379/28314 |
| _version_ | 1797829497039355904 |
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| author | Rashin Abdolhossein Harisi Hamid Reza Kobravi |
| author_facet | Rashin Abdolhossein Harisi Hamid Reza Kobravi |
| author_sort | Rashin Abdolhossein Harisi |
| collection | DOAJ |
| description | Background: In this study, three detecting approaches have been proposed and evaluated for online detection of balance situations during quiet standing. The applied methods were based on electromyography of the gastrocnemius muscles adopting the hidden Markov models. Methods: The levels of postural stability during quiet standing were regarded as the hidden states of the Markov models while the zones in which the center of pressure lies within determines the level of stability. The Markov models were trained by using the well-known Baum-Welch algorithm. The performance of a single hidden Markov model, the multiple hidden Markov model, and the multiple hidden Markov model alongside an adaptive neuro-fuzzy inference system (ANFIS), were compared as three different detecting methods. Results: The obtained results show the better and more promising performance of the method designed based on a combination of the hidden Markov models and optimized neuro-fuzzy system. Conclusion: According to the results, using the combined detecting method yielded promising results. |
| first_indexed | 2024-04-09T13:21:11Z |
| format | Article |
| id | doaj.art-8bc75c3d3fb641d7b970596f707b8cb6 |
| institution | Directory Open Access Journal |
| issn | 2383-1871 2383-2096 |
| language | English |
| last_indexed | 2024-04-09T13:21:11Z |
| publishDate | 2022-01-01 |
| publisher | Shahid Beheshti University of Medical Sciences |
| record_format | Article |
| series | International Clinical Neuroscience Journal |
| spelling | doaj.art-8bc75c3d3fb641d7b970596f707b8cb62023-05-11T06:44:00ZengShahid Beheshti University of Medical SciencesInternational Clinical Neuroscience Journal2383-18712383-20962022-01-019e3e310.34172/icnj.2022.03icnj-9190A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle MusclesRashin Abdolhossein Harisi0Hamid Reza Kobravi1Research Center of Biomedical Engineering, Islamic Azad University, Mashhad, IranResearch Center of Biomedical Engineering, Islamic Azad University, Mashhad, IranBackground: In this study, three detecting approaches have been proposed and evaluated for online detection of balance situations during quiet standing. The applied methods were based on electromyography of the gastrocnemius muscles adopting the hidden Markov models. Methods: The levels of postural stability during quiet standing were regarded as the hidden states of the Markov models while the zones in which the center of pressure lies within determines the level of stability. The Markov models were trained by using the well-known Baum-Welch algorithm. The performance of a single hidden Markov model, the multiple hidden Markov model, and the multiple hidden Markov model alongside an adaptive neuro-fuzzy inference system (ANFIS), were compared as three different detecting methods. Results: The obtained results show the better and more promising performance of the method designed based on a combination of the hidden Markov models and optimized neuro-fuzzy system. Conclusion: According to the results, using the combined detecting method yielded promising results.https://journals.sbmu.ac.ir/neuroscience/article/view/36379/28314quiet standinghidden markov modelelectromyographydynamic balance |
| spellingShingle | Rashin Abdolhossein Harisi Hamid Reza Kobravi A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles International Clinical Neuroscience Journal quiet standing hidden markov model electromyography dynamic balance |
| title | A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles |
| title_full | A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles |
| title_fullStr | A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles |
| title_full_unstemmed | A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles |
| title_short | A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles |
| title_sort | hidden markov model based detecting solution for detecting the situation of balance during unsupported standing using the electromyography of ankle muscles |
| topic | quiet standing hidden markov model electromyography dynamic balance |
| url | https://journals.sbmu.ac.ir/neuroscience/article/view/36379/28314 |
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