Classification of Center of Mass Acceleration Patterns in Older People with Knee Osteoarthritis and Fear of Falling

Arturo González-Olguín, Diego Ramos Rodríguez, Francisco Higueras Córdoba, Luis Martínez Rebolledo, Carla Taramasco and Diego Robles Cruz ()
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Arturo González-Olguín: Centro de Estudios del Movimiento Humano (CEMH), Escuela de Kinesiologia, Facultad de Salud y Odontologia, Universidad Diego Portales, Santiago 8370109, Chile
Diego Ramos Rodríguez: Hospital Regional Rancagua, Rancagua 2820000, Chile
Francisco Higueras Córdoba: ELEAM Ayen Ruca, Cunco 4890000, Chile
Luis Martínez Rebolledo: Carrera de Kinesiología Universidad Mayor, Santiago 8580745, Chile
Carla Taramasco: Facultad de Ingenieria, Universidad Andres Bello, Vina del Mar 2531015, Chile
Diego Robles Cruz: Escuela de Ingeniería Civil Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile

IJERPH, 2022, vol. 19, issue 19, 1-18

Abstract: (1) Background: The preoccupation related to the fall, also called fear of falling (FOF) by some authors is of interest in the fields of geriatrics and gerontology because it is related to the risk of falling and subsequent morbidity of falling. This study seeks to classify the acceleration patterns of the center of mass during walking in subjects with mild and moderate knee osteoarthritis (KOA) for three levels of FOF (mild, moderate, and high). (2) Method: Center-of-mass acceleration patterns were recorded in all three planes of motion for a 30-meter walk test. A convolutional neural network (CNN) was implemented for the classification of acceleration signals based on the different levels of FOF (mild, moderate, and high) for two KOA conditions (mild and moderate). (3) Results: For the three levels of FOF to fall and regardless of the degree of KOA, a precision of 0.71 was obtained. For the classification considering the three levels of FOF and only for the mild KOA condition, a precision of 0.72 was obtained. For the classification considering the three levels of FOF and only the moderate KOA condition, a precision of 0.81 was obtained, the same as in the previous case, and finally for the classification for two levels of FOF, a high vs. moderate precision of 0.78 was obtained. For high vs. low, a precision of 0.77 was obtained, and for the moderate vs. low, a precision of 0.8 was obtained. Finally, when considering both KOA conditions, a 0.74 rating was obtained. (4) Conclusions: The classification model based on deep learning (CNN) allows for the adequate discrimination of the acceleration patterns of the moderate class above the low or high FOF.

Keywords: preoccupation; fall; knee osteoarthritis; acceleration; gait; deep learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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