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

  1. Arias Poblete, Leónidas
  2. Álvarez Arangua, Sebastián 1
  3. Jerez Mayorga, Daniel 2
  4. Chamorro, Claudio 1
  5. Ferrero Hernández, Paloma 3
  6. Ferrari, Gerson 4
  7. Farías Valenzuela, Claudio 5
  1. 1 Universidad Andres Bello, Santiago, 7591538, Chile
  2. 2 University of Granada, Granada, Spain
  3. 3 Universidad SEK, Santiago 7520318, Chile
  4. 4 University of Santiago of Chile (USACH), Santiago 9170022, Chile
  5. 5 Universidad de Las Américas, Santiago 9170022, Chile
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Revista:
Sport TK: revista euroamericana de ciencias del deporte

ISSN: 2340-8812,  2254-4070

Año de publicación: 2023

Título del ejemplar: Aplicaciones de las ciencias de la actividad física y la medicina del deporte para el rendimiento humano

Número: 12

Tipo: Artículo

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Otras publicaciones en: Sport TK: revista euroamericana de ciencias del deporte

Resumen

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.

Referencias bibliográficas

  • Agostini, V., Nascimbeni, A., Gaffuri, A., Imazio, P., Benedetti, M., & Knaflitz, M. (2010). Normative EMG activation patterns of school-age children during gait. Gait & posture, 32(3), 285-289.
  • Beauchet, O., Fantino, B., Allali, G., Muir, S., Montero-Odasso, M., & Annweiler, C. (2011). Timed Up and Go test and risk of falls in older adults: a systematic review. The journal of nutrition, health & aging, 15(10), 933-938.
  • Berg, W. P., Alessio, H. M., Mills, E. M., & Tong, C. (1997). Circumstances and consequences of falls in independent community-dwelling older adults. Age and ageing, 26(4), 261-268.
  • Bijlsma, A., Meskers, C., Ling, C., Narici, M., Kurrle, S., Cameron, I., Westendorp, R., & Maier, A. (2013). Defining sarcopenia: the impact of different diagnostic criteria on the prevalence of sarcopenia in a large middle aged cohort. Age, 35(3), 871-881.
  • Cali, C. M., & Kiel, D. P. (1995). An epidemiologic study of fall‐related fractures among institutionalized older people. Journal of the American Geriatrics Society, 43(12), 1336-1340.
  • De Groote, F., & Falisse, A. (2021). Perspective on musculoskeletal modelling and predictive simulations of human movement to assess the neuromechanics of gait. Proceedings. Biological sciences, 288(1946), 20202432. https://doi.org/10.1098/rspb.2020.2432
  • Frigo, C., & Crenna, P. (2009). Multichannel SEMG in clinical gait analysis: a review and state-of-the-art. Clinical Biomechanics, 24(3), 236-245.
  • González, G., Marín, P. P., & Pereira, G. (2001). Características de las caídas en el adulto mayor que vive en la comunidad. Revista médica de Chile, 129(9), 1021-1030.
  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. . (2009). The WEKA data mining software: an update. ACM SIGKDD explorations, newsletter, 11(1), 10-18.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics. Springer New York.
  • Jacquelin Perry, M. (2010). Gait analysis: normal and pathological function. New Jersey: SLACK.
  • Kyrdalen, I. L., Thingstad, P., Sandvik, L., & Ormstad, H. (2019). Associations between gait speed and well-known fall risk factors among community-dwelling older adults. Physiotherapy research international: the journal for researchers and clinicians in physical therapy, 24(1), e1743. https://doi.org/10.1002/pri.1743
  • Lai, D. T., Levinger, P., Begg, R. K., Gilleard, W. L., & Palaniswami, M. (2009). Automatic recognition of gait patterns exhibiting patellofemoral pain syndrome using a support vector machine approach. IEEE Transactions on Information Technology in Biomedicine, 13(5), 810-817.
  • Landi, F., Liperoti, R., Russo, A., Giovannini, S., Tosato, M., Capoluongo, E., Bernabei, R., & Onder, G. (2012). Sarcopenia as a risk factor for falls in elderly individuals: results from the ilSIRENTE study. Clinical nutrition, 31(5), 652-658.
  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 159-174.
  • Lauretani, F., Russo, C. R., Bandinelli, S., Bartali, B., Cavazzini, C., Di Iorio, A., Corsi, A. M., Rantanen, T., Guralnik, J. M., & Ferrucci, L. (2003). Age-associated changes in skeletal muscles and their effect on mobility: an operational diagnosis of sarcopenia. Journal of applied physiology, 95(5), 1851-1860.
  • Lera, L., Albala, C., Ángel, B., Sánchez, H., Picrin, Y., Hormazabal, M. J., & Quiero, A. (2014). Anthropometric model for the prediction of appendicular skeletal muscle mass in Chilean older adults. Nutrición Hospitalaria, 29(3), 611-617.
  • López, R., Mancilla, E., Villalobos, A., & Herrera, P. (2010). Manual de prevención de caídas en el adulto mayor. Gobierno de Chile, Ministerio de salud.
  • Lord, S. R., Lloyd, D. G., & Keung Li, S. (1996). Sensori-motor function, gait patterns and falls in community-dwelling women. Age and ageing, 25(4), 292-299.
  • MacAulay, R. K., Boeve, A., D'Errico, L., Halpin, A., Szeles, D. M., & Wagner, M. T. (2022). Slower gait speed increases risk of falling in older adults with depression and cognitive complaints. Psychology, health & medicine, 27(7), 1576–1581. https://doi.org/10.1080/13548506.2021.1903056
  • Menz, H. B., Lord, S. R., & Fitzpatrick, R. C. (2003). Acceleration patterns of the head and pelvis when walking are associated with risk of falling in community-dwelling older people. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 58(5), M446-M452.
  • Norton, R., Campbell, A. J., Lee‐Joe, T., Robinson, E., & Butler, M. (1997). Circumstances of falls resulting in hip fractures among older people. Journal of the American Geriatrics Society, 45(9), 1108-1112.
  • Papagiannis, G. I., Triantafyllou, A. I., Roumpelakis, I. M., Zampeli, F., Garyfallia Eleni, P., Koulouvaris, P., Papadopoulos, E. C., Papagelopoulos, P. J., & Babis, G. C. (2019). Methodology of surface electromyography in gait analysis: review of the literature. Journal of medical engineering & technology, 43(1), 59–65. https://doi.org/10.1080/03091902.2019.1609610
  • Ronthal M. (2019). Gait Disorders and Falls in the Elderly. The Medical clinics of North America, 103(2), 203–213. https://doi.org/10.1016/j.mcna.2018.10.010
  • Rydwik, E., Bergland, A., Forsén, L., & Frändin, K. (2011). Psychometric properties of timed up and go in elderly people: a systematic review. Physical & Occupational Therapy in Geriatrics, 29(2), 102-125.
  • Salari, N., Darvishi, N., Ahmadipanah, M., Shohaimi, S., & Mohammadi, M. (2022). Global prevalence of falls in the older adults: a comprehensive systematic review and meta-analysis. Journal of orthopaedic surgery and research, 17(1), 334. https://doi.org/10.1186/s13018-022-03222-1
  • Schoene, D., Wu, S. M. S., Mikolaizak, A. S., Menant, J. C., Smith, S. T., Delbaere, K., & Lord, S. R. (2013). Discriminative ability and predictive validity of the timed Up and Go test in identifying older people who fall: systematic review and meta‐analysis. Journal of the American Geriatrics Society, 61(2), 202-208.
  • SENIAM. (2020). Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles [Internet]. Enschede, Netherlands. http://www.seniam.org.
  • Tinetti, M. E., & Williams, C. S. (1997). Falls, injuries due to falls, and the risk of admission to a nursing home. New England journal of medicine, 337(18), 1279-1284.
  • Vapnik, V. N. (1995). The nature of statistical learning. Theory.
  • Wang, H., Shao, Y., Zhou, S., Zhang, C., & Xiu, N. (2022). Support Vector Machine Classifier via L0/1 Soft-Margin Loss. IEEE transactions on pattern analysis and machine intelligence, 44(10), 7253–7265. https://doi.org/10.1109/TPAMI.2021.3092177
  • Yack, H. J., & Berger, R. C. (1993). Dynamic stability in the elderly: identifying a possible measure. Journal of gerontology, 48(5), M225-M230.
  • Yeung, S. S. Y., Reijnierse, E. M., Pham, V. K., Trappenburg, M. C., Lim, W. K., Meskers, C. G. M., & Maier, A. B. (2019). Sarcopenia and its association with falls and fractures in older adults: A systematic review and meta-analysis. Journal of cachexia, sarcopenia and muscle, 10(3), 485–500. https://doi.org/10.1002/jcsm.12411
  • Zavaljevski, N., Stevens, F. J., & Reifman, J. (2002). Support vector machines with selective kernel scaling for protein classification and identification of key amino acid positions. Bioinformatics, 18(5), 689-696.
Fuente de los datos: Dialnet