인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Energy expenditure (EE) assessment is crucial in both sports science and health management. However, current EE prediction models often overlook individual differences and lack dynamic correlation analysis between multi-modal data and EE. Building upon previous research, this study proposes a personalized dynamic-static feature fusion framework, which integrates two types of information to improve energy expenditure (EE) prediction during incremental load exercise: dynamic signals (physiological signals recorded continuously during exercise, such as tri-axial acceleration and electrocardiography [ECG]) and static physiological metrics (stable individual traits measured at rest, such as BMI, body-fat percentage, resting heart rate, and resting oxygen uptake [VO<sub>2</sub>]). These two feature sets were combined through a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) neural network architecture. CNN layers extract local temporal patterns from dynamic signals, and LSTM layers model temporal dependencies over longer intervals. The model prediction performance was evaluated using root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE) and Bland-Altman plots, and the results show that the CNN + LSTM model significantly outperforms both the traditional autoregressive (AR) linear model and the LSTM model that uses only a single modality (acceleration or ECG). Analysis of feature values and SHAP values revealed that accelerometer features played a dominant role in EE prediction during moderate-to-high intensity exercise. As exercise intensity increased, the contribution of ECG features gradually increased, with ECG features dominating during high-intensity exercise, demonstrating the complementary effect and dual contribution of these two types of features in EE prediction at different exercise intensities. This study demonstrates that personalized dynamic-static feature fusion can effectively predict EE during incremental exercise tests and analyzes the dynamic changes in the contribution of different features across different intensity ranges, providing a theoretical basis and methodological reference for related research.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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