Frontiers in physiology · 2026
Predicting physical activity energy expenditure, types, and intensity using long short-term memory networks in aged 6-18 cross-stage populations.
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Jiang Lei, Gao Yanan, Zhao Chunyu, Guo Zice, Huang Zhengyuan, Fan Qilin, Lai Junjun, Su Rufeng, Zhang Youwei, Zhang Jinxi
Аннотация
BACKGROUND: Accurately estimating physical activity (PA) and energy expenditure of children and adolescents cross-stage are key issues in sports medicine and beyond.
OBJECTIVE: This study aims to develop and test a long short-term memory (LSTM) network to predict physical activity energy expenditure (PAEE), types, and intensity using accelerometer data collected in children and adolescents.
METHODS: A total of 120 healthy participants aged 6-18 years completed 13 PA tasks while wearing triaxial accelerometers at 9 body sites and simultaneously wore the portable metabolic analyzer. 59 features were extracted from accelerometer signals, combined with demographic data to build LSTM models for cross-stage and single-stage (childhood, early and late adolescence), respectively, and compared with the results of 3 ensemble learning (random forest, extreme gradient boosting, and light gradient boosting machine).
RESULTS: The RMSE for predicting PAEE across and single stages were about 0.55 metabolic equivalents (METs) to 0.73 METs, and an average reduction of about 55% in RMSE over ensemble learning. The RMSE for predicting energy expenditure of a specific PA type was averaged about 0.38 METs to 0.49 METs, and an average reduction of about 60% in RMSE over ensemble learning. The recall for predicting PA types was about 90% to 93%, with an average relative improvement of about 70% over ensemble learning. The recall for predicted PA intensity was about 90% to 93%, with an average relative improvement of about 32% over ensemble learning.
CONCLUSIONS: LSTM showed strong generalizability and robustness in cross-stage PA monitoring, offering an accurate and scalable solution.
OBJECTIVE: This study aims to develop and test a long short-term memory (LSTM) network to predict physical activity energy expenditure (PAEE), types, and intensity using accelerometer data collected in children and adolescents.
METHODS: A total of 120 healthy participants aged 6-18 years completed 13 PA tasks while wearing triaxial accelerometers at 9 body sites and simultaneously wore the portable metabolic analyzer. 59 features were extracted from accelerometer signals, combined with demographic data to build LSTM models for cross-stage and single-stage (childhood, early and late adolescence), respectively, and compared with the results of 3 ensemble learning (random forest, extreme gradient boosting, and light gradient boosting machine).
RESULTS: The RMSE for predicting PAEE across and single stages were about 0.55 metabolic equivalents (METs) to 0.73 METs, and an average reduction of about 55% in RMSE over ensemble learning. The RMSE for predicting energy expenditure of a specific PA type was averaged about 0.38 METs to 0.49 METs, and an average reduction of about 60% in RMSE over ensemble learning. The recall for predicting PA types was about 90% to 93%, with an average relative improvement of about 70% over ensemble learning. The recall for predicted PA intensity was about 90% to 93%, with an average relative improvement of about 32% over ensemble learning.
CONCLUSIONS: LSTM showed strong generalizability and robustness in cross-stage PA monitoring, offering an accurate and scalable solution.