Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence

Authors

  • Leónidas Arias-Poblete Exercise and Rehabilitation Sciences Institute, School of Physical Therapy, Faculty of Rehabilitation
  • Sebastián Álvarez‐Arangua Exercise and Rehabilitation Sciences Institute, School of Physical Therapy, Faculty of Rehabilitation Sciences, Universidad Andres Bello, Santiago, 7591538, Chile
  • Daniel Jerez-Mayorga Exercise 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, Spain
  • Claudio Chamorro Exercise and Rehabilitation Sciences Institute, School of Physical Therapy, Faculty of Rehabilitation Sciences, Universidad Andres Bello, Santiago, 7591538, Chile
  • Paloma Ferrero‐Hernández Facultad de Educación y Cultura, Universidad SEK, Santiago 7520318, Chile
  • Gerson Ferrari Facultad 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, Chile
  • Claudio Farías‐Valenzuela Instituto del Deporte, Universidad de Las Américas, Santiago 9170022, Chile
DOI: https://doi.org/10.6018/sportk.575281
Keywords: Older adults, Fall risk, Gait, Electromyography, Support vector machines

Abstract

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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Published
25-06-2023
How to Cite
Arias-Poblete, L. ., Álvarez‐Arangua, S. ., Jerez-Mayorga, D. ., Chamorro, C. ., Ferrero‐Hernández, P. ., Ferrari, G. ., & Farías‐Valenzuela, C. . (2023). Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence. SPORT TK-EuroAmerican Journal of Sport Sciences, 12, 5. https://doi.org/10.6018/sportk.575281
Issue
Vol 12 (2023): Supplement 4
Section
Articles

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The works and papers that are published in this Journal are subject to the following terms:

1. The Publication Service of the University of Murcia (the publisher) has the Publication Rights (Copyright) to the published papers and works, and favors and permits the reusing of the same under the license indicated in point 2.

© Servicio de Publicaciones, Universidad de Murcia, 2013

2. The papers and works are to be published in the digital edition of the Journal under the license Creative Commons Reconocimiento-No Comercial-Sin Obra Derivada 3.0 España (legal text). The copying, using, spreading, transmitting and publicly displaying of the papers, works or publication are permitted as long as: i) the authors and original sources (Journal, publisher and URL of the publication) are quoted; ii) it is not used for commercial benefit; iii) the existence and specifications of this users license are mentioned.

3. Conditions of Self-Archiving. It is permitted and encouraged that the authors spread electronically the pre-print (before printing) and/or post-print (the revised, evaluated and accepted) versions of their papers or works before their publication since this favors their circulation and early diffusion and therefore can help increase their citation and quotation, and also there reach through the academic community.