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학술지 Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors
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저자
김정균, 배명남, 이강복, 홍상기
발행일
202103
출처
Sensors, v.21 no.5, pp.1-17
ISSN
1424-8220
출판사
MDPI
DOI
https://dx.doi.org/10.3390/s21051786
협약과제
21IR1700, 공간정보 기반 실감 재난관리 맞춤형 콘텐츠 제공 기술개발, 이강복
초록
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.
키워드
Gait analysis, Gait parameter, Inertial measurement units, Sarcopenia, Shapley Additive explanations, Smart insole, XAI
KSP 제안 키워드
Angular Velocity, Automatic feature selection, Classification method, Feature extractioN, Feature selection(FS), Gait Analysis, Grip strength, Inertial measurement units(IMUs), Inertial sensors, Knowledge-based, Learning methods
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