Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence
Introduction: The tests used to classify older adults at risk of falls are questioned in literature. Tools from the field of artificial intelligence are an alternative to classify older adults more precisely. Objective: To identify the risk of falls in the elderly through electromyographic signals...
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| Format: | Article |
| Language: | English |
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Universidad de Murcia
2023-06-01
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| Series: | Sport TK |
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| Online Access: | https://revistas.um.es/sportk/article/view/575281 |
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| author | Leónidas Arias-Poblete Sebastián Álvarez‐Arangua Daniel Jerez-Mayorga Claudio Chamorro Paloma Ferrero‐Hernández Gerson Ferrari Claudio Farías‐Valenzuela |
| author_facet | Leónidas Arias-Poblete Sebastián Álvarez‐Arangua Daniel Jerez-Mayorga Claudio Chamorro Paloma Ferrero‐Hernández Gerson Ferrari Claudio Farías‐Valenzuela |
| author_sort | Leónidas Arias-Poblete |
| collection | DOAJ |
| description |
Introduction: The tests used to classify older adults at risk of falls are questioned in literature. Tools from the field of artificial intelligence are an alternative to classify older adults more precisely. Objective: To identify the risk of falls in the elderly through electromyographic signals of the lower limb, using tools from the field of artificial intelligence. Methods: A descriptive study design was used. The unit of analysis was made up of 32 older adults (16 with and 16 without risk of falls). The electrical activity of the lower limb muscles was recorded during the functional walking gesture. The cycles obtained were divided into training and validation sets, and then from the amplitude variable, select attributes using the Weka software. Finally, the Support Vector Machines (SVM) classifier was implemented. Results: A classifier of two classes (elderly adults with and without risk of falls) based on SVM was built, whose performance was: Kappa index 0.97 (almost perfect agreement strength), sensitivity 97%, specificity 100%. Conclusions: The SVM artificial intelligence technique applied to the analysis of lower limb electromyographic signals during walking can be considered a precision tool of diagnostic, monitoring and follow-up for older adults with and without risk of falls.
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| first_indexed | 2024-03-13T03:07:08Z |
| format | Article |
| id | doaj.art-c3b32d39e1cf47f2a5b43715a3356b46 |
| institution | Directory Open Access Journal |
| issn | 2340-8812 |
| language | English |
| last_indexed | 2024-03-13T03:07:08Z |
| publishDate | 2023-06-01 |
| publisher | Universidad de Murcia |
| record_format | Article |
| series | Sport TK |
| spelling | doaj.art-c3b32d39e1cf47f2a5b43715a3356b462023-06-26T21:02:42ZengUniversidad de MurciaSport TK2340-88122023-06-011210.6018/sportk.575281Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligenceLeónidas Arias-Poblete0Sebastián Álvarez‐Arangua1Daniel Jerez-Mayorga2Claudio Chamorro3Paloma Ferrero‐Hernández4Gerson Ferrari5Claudio Farías‐Valenzuela6Exercise and Rehabilitation Sciences Institute, School of Physical Therapy, Faculty of Rehabilitation Exercise and Rehabilitation Sciences Institute, School of Physical Therapy, Faculty of Rehabilitation Sciences, Universidad Andres Bello, Santiago, 7591538, ChileExercise and Rehabilitation Sciences Institute, School of Physical Therapy, Faculty of Rehabilitation Sciences, Universidad Andres Bello, Santiago, 7591538, Chile | Strength & Conditioning Laboratory, CTS-642 Research Group, Department Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, SpainExercise and Rehabilitation Sciences Institute, School of Physical Therapy, Faculty of Rehabilitation Sciences, Universidad Andres Bello, Santiago, 7591538, ChileFacultad de Educación y Cultura, Universidad SEK, Santiago 7520318, ChileFacultad de Ciencias de la Salud, Universidad Autónoma de Chile, Providencia 7500912, Chile | Sciences of Physical Activity, Sports and Health School, University of Santiago of Chile (USACH), Santiago 9170022, ChileInstituto del Deporte, Universidad de Las Américas, Santiago 9170022, Chile Introduction: The tests used to classify older adults at risk of falls are questioned in literature. Tools from the field of artificial intelligence are an alternative to classify older adults more precisely. Objective: To identify the risk of falls in the elderly through electromyographic signals of the lower limb, using tools from the field of artificial intelligence. Methods: A descriptive study design was used. The unit of analysis was made up of 32 older adults (16 with and 16 without risk of falls). The electrical activity of the lower limb muscles was recorded during the functional walking gesture. The cycles obtained were divided into training and validation sets, and then from the amplitude variable, select attributes using the Weka software. Finally, the Support Vector Machines (SVM) classifier was implemented. Results: A classifier of two classes (elderly adults with and without risk of falls) based on SVM was built, whose performance was: Kappa index 0.97 (almost perfect agreement strength), sensitivity 97%, specificity 100%. Conclusions: The SVM artificial intelligence technique applied to the analysis of lower limb electromyographic signals during walking can be considered a precision tool of diagnostic, monitoring and follow-up for older adults with and without risk of falls. https://revistas.um.es/sportk/article/view/575281Older adultsFall riskGaitElectromyographySupport vector machines |
| spellingShingle | Leónidas Arias-Poblete Sebastián Álvarez‐Arangua Daniel Jerez-Mayorga Claudio Chamorro Paloma Ferrero‐Hernández Gerson Ferrari Claudio Farías‐Valenzuela Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence Sport TK Older adults Fall risk Gait Electromyography Support vector machines |
| title | Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence |
| title_full | Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence |
| title_fullStr | Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence |
| title_full_unstemmed | Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence |
| title_short | Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence |
| title_sort | fall risk detection mechanism in the elderly based on electromyographic signals through the use of artificial intelligence |
| topic | Older adults Fall risk Gait Electromyography Support vector machines |
| url | https://revistas.um.es/sportk/article/view/575281 |
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