← 研踪 PaperTrailarXiv:2611.14849 · v1
Generalize Then Personalize: Wearable-Based Heart Rate Forecasting Under Scarce Ruck-March Data
首次提交 2026-11-11 07:58
版本更新 2026-11-11 07:58
雷达观测 2026-11-13 10:09
摘要
Heart rate (HR) forecasting may provide advance information about near-future cardiovascular response, potentially supporting physiological monitoring and workload-management decisions during ruck marching. However, robust model development is constrained by the difficulty of collecting large-scale ruck-march datasets, creating a scarce-data problem in which models must generalize across participants while adapting to individuals from limited personal data. To address this problem, we benchmark population-only training (POT), personalized-from-scratch training (PFST), and population-pretrained fine-tuning (PPFT) for HR forecasting during loaded ruck marching using data from eight active-duty U.S. Army medics across thirteen sessions. LSTM, Attention-LSTM, and CNN-LSTM backbones were evaluated against a persistence baseline at 30 s and 60 s horizons. Across all neural architectures and horizons, PPFT yielded lower mean MAE, RMSE, and endpoint MAE than both POT and PFST. Participant-level paired analysis after averaging across three training seeds showed that, relative to PFST, PPFT reduced trajectory MAE by 2.22-4.30 bpm and endpoint MAE by 1.24-2.88 bpm. All six trajectory-MAE comparisons remained significant after Holm adjustment ($p_{adj}$ = 0.0469), while four of six endpoint-MAE comparisons remained significant after adjustment. At 30 s, persistence achieved the lowest MAE (3.555 $\pm$ 1.274 bpm), while CNN-LSTM PPFT achieved the lowest RMSE (4.669 $\pm$ 1.916 bpm); LSTM PPFT achieved the lowest endpoint MAE (5.093 $\pm$ 2.365 bpm). At 60 s, Attention-LSTM PPFT achieved the lowest MAE (5.573 $\pm$ 1.939 bpm) and RMSE (7.134 $\pm$ 2.260 bpm), while CNN-LSTM PPFT achieved the lowest endpoint MAE (7.553 $\pm$ 2.357 bpm). These results suggest that population pretraining followed by participant-specific fine-tuning may provide a practical strategy for scarce-data wearable HR forecasting.
作者备注：IEEE BIBM 2026
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v1 · 2026-11-11 07:58 · 最新观测版本
Generalize Then Personalize: Wearable-Based Heart Rate Forecasting Under Scarce Ruck-March Data
Heart rate (HR) forecasting may provide advance information about near-future cardiovascular response, potentially supporting physiological monitoring and workload-management decisions during ruck marching. However, robust model development is constrained by the difficulty of collecting large-scale ruck-march datasets, creating a scarce-data problem in which models must generalize across participants while adapting to individuals from limited personal data. To address this problem, we benchmark population-only training (POT), personalized-from-scratch training (PFST), and population-pretrained fine-tuning (PPFT) for HR forecasting during loaded ruck marching using data from eight active-duty U.S. Army medics across thirteen sessions. LSTM, Attention-LSTM, and CNN-LSTM backbones were evaluated against a persistence baseline at 30 s and 60 s horizons. Across all neural architectures and horizons, PPFT yielded lower mean MAE, RMSE, and endpoint MAE than both POT and PFST. Participant-level paired analysis after averaging across three training seeds showed that, relative to PFST, PPFT reduced trajectory MAE by 2.22-4.30 bpm and endpoint MAE by 1.24-2.88 bpm. All six trajectory-MAE comparisons remained significant after Holm adjustment ($p_{adj}$ = 0.0469), while four of six endpoint-MAE comparisons remained significant after adjustment. At 30 s, persistence achieved the lowest MAE (3.555 $\pm$ 1.274 bpm), while CNN-LSTM PPFT achieved the lowest RMSE (4.669 $\pm$ 1.916 bpm); LSTM PPFT achieved the lowest endpoint MAE (5.093 $\pm$ 2.365 bpm). At 60 s, Attention-LSTM PPFT achieved the lowest MAE (5.573 $\pm$ 1.939 bpm) and RMSE (7.134 $\pm$ 2.260 bpm), while CNN-LSTM PPFT achieved the lowest endpoint MAE (7.553 $\pm$ 2.357 bpm). These results suggest that population pretraining followed by participant-specific fine-tuning may provide a practical strategy for scarce-data wearable HR forecasting.
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