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Article

Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors

1
Department of Computer Software, ICT, University of Science and Technology, Daejeon 34113, Korea
2
Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Cristina P. Santos
Sensors 2021, 21(5), 1786; https://doi.org/10.3390/s21051786
Received: 29 January 2021 / Revised: 23 February 2021 / Accepted: 25 February 2021 / Published: 4 March 2021
(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)
Sarcopenia can cause various senile diseases and is a major factor associated with the quality of life in old age. To diagnose, assess, and monitor muscle loss in daily life, 10 sarcopenia and 10 normal subjects were selected using lean mass index and grip strength, and their gait signals obtained from inertial sensor-based gait devices were analyzed. Given that the inertial sensor can measure the acceleration and angular velocity, it is highly useful in the kinematic analysis of walking. This study detected spatial-temporal parameters used in clinical practice and descriptive statistical parameters for all seven gait phases for detailed analyses. To increase the accuracy of sarcopenia identification, we used Shapley Additive explanations to select important parameters that facilitated high classification accuracy. Support vector machines (SVM), random forest, and multilayer perceptron are classification methods that require traditional feature extraction, whereas deep learning methods use raw data as input to identify sarcopenia. As a result, the input that used the descriptive statistical parameters for the seven gait phases obtained higher accuracy. The knowledge-based gait parameter detection was more accurate in identifying sarcopenia than automatic feature selection using deep learning. The highest accuracy of 95% was achieved using an SVM model with 20 descriptive statistical parameters. Our results indicate that sarcopenia can be monitored with a wearable device in daily life. View Full-Text
Keywords: sarcopenia; gait analysis; gait parameter; XAI; inertial measurement units; smart insole; Shapley Additive explanations sarcopenia; gait analysis; gait parameter; XAI; inertial measurement units; smart insole; Shapley Additive explanations
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MDPI and ACS Style

Kim, J.-K.; Bae, M.-N.; Lee, K.B.; Hong, S.G. Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors. Sensors 2021, 21, 1786. https://doi.org/10.3390/s21051786

AMA Style

Kim J-K, Bae M-N, Lee KB, Hong SG. Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors. Sensors. 2021; 21(5):1786. https://doi.org/10.3390/s21051786

Chicago/Turabian Style

Kim, Jeong-Kyun, Myung-Nam Bae, Kang B. Lee, and Sang G. Hong. 2021. "Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors" Sensors 21, no. 5: 1786. https://doi.org/10.3390/s21051786

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