Examinando por Autor "Chamorro, Claudio"
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Ítem Absolute reliability and concurrent validity of hand held dynamometry and isokinetic dynamometry in the hip, knee and ankle joint: Systematic review and meta-analysis(De Gruyter Open Ltd, 2017) Chamorro, Claudio; Armijo-Olivo, Susan; De La Fuente, Carlos d,; Fuentes, Javiera; Javier Chirosa, Luis fThe purpose of the study is to establish absolute reliability and concurrent validity between hand-held dynamometers (HHDs) and isokinetic dynamometers (IDs) in lower extremity peak torque assessment. Medline, Embase, CINAHL databases were searched for studies related to psychometric properties in muscle dynamometry. Studies considering standard error of measurement SEM (%) or limit of agreement LOA (%) expressed as percentage of the mean, were considered to establish absolute reliability while studies using intra-class correlation coefficient (ICC) were considered to establish concurrent validity between dynamometers. In total, 17 studies were included in the meta-analysis. The COSMIN checklist classified them between fair and poor. Using HHDs, knee extension LOA (%) was 33.59%, 95% confidence interval (CI) 23.91 to 43.26 and ankle plantar flexion LOA (%) was 48.87%, CI 35.19 to 62.56. Using IDs, hip adduction and extension; knee flexion and extension; and ankle dorsiflexion showed LOA (%) under 15%. Lower hip, knee, and ankle LOA (%) were obtained using an ID compared to HHD. ICC between devices ranged between 0.62, CI (0.37 to 0.87) for ankle dorsiflexion to 0.94, IC (0.91to 0.98) for hip adduction. Very high correlation were found for hip adductors and hip flexors and moderate correlations for knee flexors/extensors and ankle plantar/dorsiflexors. © 2017 Claudio Chamorro et al. 2017.Ítem Fall risk detection mechanism in the elderly, based on electromyographic signals, through the use of artificial intelligence(Universidad de Murcia, 2023) Arias-Poblete, Leónidas; Álvarez‐Arangua, Sebastián; Jerez-Mayorga, Daniel; Chamorro, Claudio; Ferrero‐Hernández, Paloma; Ferrari, Gerson; Farías‐Valenzuela, ClaudioIntroduction: 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. © Copyright 2023: Publication Service of the University of Murcia, Murcia, Spain.