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. 2022 Oct 8;19(19):12890.
doi: 10.3390/ijerph191912890.

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

Arturo González-Olguín  1   2 , Diego Ramos Rodríguez  3 , Francisco Higueras Córdoba  4 , Luis Martínez Rebolledo  5 , Carla Taramasco  6   7 , Diego Robles Cruz  8   9
Affiliations

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

Arturo González-Olguín et al. Int J Environ Res Public Health. .

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: acceleration; deep learning; fall; gait; knee osteoarthritis; preoccupation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Diagram of the CNN model.
Figure 2
Figure 2
Gait characteristics according to degree of FOF. Variable values expressed in z-score.
Figure 3
Figure 3
Confusion matrix, accuracy, and loss function for the total dataset (KOA mild and moderate) for the three levels of FOF.
Figure 4
Figure 4
Confusion matrix, accuracy, and loss function for mild osteoarthritis and the three levels of FOF.
Figure 5
Figure 5
Confusion matrix, accuracy, and loss function for moderate osteoarthritis and the three levels of FOF.
Figure 6
Figure 6
Confusion matrix, accuracy, and loss function for the total dataset for two levels of FOF (low vs. moderate).
Figure 7
Figure 7
Confusion matrix, accuracy, and loss function for the total dataset for two levels of FOF (moderate vs. high).
Figure 8
Figure 8
Confusion matrix, accuracy, and loss function for the total dataset for two levels of fear of fall (low vs. high).
Figure 9
Figure 9
Confusion matrix, accuracy, and loss function for KOA mild vs. moderate.
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