用深度学习建模攀岩时恐惧与肌肉活动的关系，发现疲劳加剧恐惧。
The Climber's Grip -- Personalized Deep Learning Models for Fear and Muscle Activity in Climbing
- 结合统计模型与深度学习，个性化建模攀岩者心理生理互动
- 随机效应使误差指标均下降，证明个体差异重要性
- 适合运动科学、人机交互及个性化训练系统研究者
攀岩是一项融合体能与情绪认知挑战的复杂运动。先锋攀登因坠落距离更大，可能引发更高感知风险与恐惧。本研究通过统计建模与深度学习方法，探究攀岩者在先锋与顶绳攀登中感知恐惧与肌电活动的关联。实验招募19名攀岩者，采集其在不同攀爬阶段的肌电（EMG）、心电（ECG）与手臂运动数据，并记录主观恐惧评分。采用线性混合效应模型分析恐惧与生理指标的关系，进一步引入随机效应的深度学习模型以捕捉非线性动态。结果表明，加入随机效应显著降低均方误差（MSE）、平均绝对误差（MAE）和均方根误差（RMSE）。尤其在先锋攀登中，肌肉疲劳与恐惧感显著正相关。研究展示了统计与深度学习结合在建模心理-生理交互中的潜力。
原文摘要 · Abstract (English)
Climbing is a multifaceted sport that combines physical demands and emotional and cognitive challenges. Ascent styles differ in fall distance with lead climbing involving larger falls than top rope climbing, which may result in different perceived risk and fear. In this study, we investigated the psychophysiological relationship between perceived fear and muscle activity in climbers using a combination of statistical modeling and deep learning techniques. We conducted an experiment with 19 climbers, collecting electromyography (EMG), electrocardiography (ECG) and arm motion data during lead and top rope climbing. Perceived fear ratings were collected for the different phases of the climb. Using a linear mixed-effects model, we analyzed the relationships between perceived fear and physiological measures. To capture the non-linear dynamics of this relationship, we extended our analysis to deep learning models and integrated random effects for a personalized modeling approach. Our results showed that random effects improved model performance of the mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE). The results showed that muscle fatigue correlates significantly with increased fear during \textit{lead climbing}. This study highlights the potential of combining statistical and deep learning approaches for modeling the interplay between psychological and physiological states during climbing.
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