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학술지 Explainable Artificial Intelligence and Wearable Sensor-Based Gait Analysis to Identify Patients with Osteopenia and Sarcopenia in Daily Life
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저자
김정균, 배명남, 이강복, 김재철, 홍상기
발행일
202203
출처
Biosensors, v.12 no.3, pp.1-22
ISSN
2079-6374
출판사
MDPI
DOI
https://dx.doi.org/10.3390/bios12030167
협약과제
21JR3100, 공공기반 재활운동 빅데이터 플랫폼 기술개발, 김재철
초록
Osteopenia and sarcopenia can cause various senile diseases and are key factors related to the quality of life in old age. There is need for portable tools and methods that can analyze osteopenia and sarcopenia risks during daily life, rather than requiring a specialized hospital setting. Gait is a suitable indicator of musculoskeletal diseases; therefore, we analyzed the gait signal obtained from an inertial-sensor-based wearable gait device as a tool to manage bone loss and muscle loss in daily life. To analyze the inertial-sensor-based gait, the inertial signal was classified into seven gait phases, and descriptive statistical parameters were obtained for each gait phase. Subsequently, explainable artificial intelligence was utilized to analyze the contribution and importance of descriptive statistical parameters on osteopenia and sarcopenia. It was found that XGBoost yielded a high accuracy of 88.69% for osteopenia, whereas the random forest approach showed a high accuracy of 93.75% for sarcopenia. Transfer learning with a ResNet backbone exhibited appropriate performance but showed lower accuracy than the descriptive statistical parameter-based identification result. The proposed gait analysis method confirmed high classification accuracy and the statistical significance of gait factors that can be used for osteopenia and sarcopenia management.
KSP 제안 키워드
Analysis method, Bone loss, Gait Analysis, High accuracy, Inertial sensors, Key factor, Musculoskeletal Diseases, Old age, Quality of life, Random forest, Statistical Parameters
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