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

• Arias Poblete, Leónidas ; Álvarez Arangua, Sebastián ^[1] ; Jerez Mayorga, Daniel ^[2] ; Chamorro, Claudio ^[1] ; Ferrero Hernández, Paloma ^[4] ; Ferrari, Gerson ^[5] ; Farías Valenzuela, Claudio ^[3]
  1. [1] Universidad Andrés Bello

     Universidad Andrés Bello

     Santiago, Chile

     
  2. [2] Universidad de Granada

     Universidad de Granada

     Granada, España

     
  3. [3] Universidad de las Américas

     Universidad de las Américas

     Santiago, Chile

     
  4. [4] Universidad SEK, Santiago 7520318, Chile
  5. [5] University of Santiago of Chile (USACH), Santiago 9170022, Chile

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• Localización: Sport TK: revista euroamericana de ciencias del deporte, ISSN 2254-4070, ISSN-e 2340-8812, Nº. Extra 12, 4 (Suplemento), 2023 (Ejemplar dedicado a: Aplicaciones de las ciencias de la actividad física y la medicina del deporte para el rendimiento humano)
• Idioma: inglés
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  • 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.