脊髓损伤对分级运动期间皮肤温度调节的影响:基于元启发式数据挖掘的预测
Ming Xia Yang1, Tao Yan1, Miaomiao Sun1
1Qinhuangdao Vocational and Technical College, Qinhuangdao, Hebei, China.
Computer methods in biomechanics and biomedical engineering
|February 4, 2026
概括
脊髓损伤 (SCI) 损害了运动期间的体温调节. 先进的AI模型准确地预测了皮肤和核心温度,揭示了影响SCI个体温度调节的关键因素.
科学领域:
- 生理学 生理学 生理学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 在运动期间的温度调节在脊髓损伤 (SCI) 患者中受到损害.
- 皮肤温度作为温度调节反应的非侵入性指标.
- 了解这些反应对于管理SCI群体的运动至关重要.
研究的目的:
- 开发和验证先进的AI模型,用于预测SCI患者在运动期间的皮肤和核心体温动态.
- 为了比较极端学习机器 (ELM) 模型的不同优化算法的预测准确度.
- 确定影响SCI温度调节的关键生理和人口因素.
主要方法:
- 混合极端学习机器 (ELM) 模型使用狮子,龙和进化战略算法开发和优化.
- 这些模型预测了32名参与者 (16名SCI,16名对照) 进行分级臂运动时的皮肤和核心温度.
- 用特征重要性和SHAP分析来确定预测意义.
主要成果:
- 龙优化的ELM模型表现出卓越的准确性 (R2 = 99.705,RMSE = 0.014).
- 预测和测量核心体温之间没有发现显著差异 (p > 0.05).
- 峰值功率输出,参与者组 (SCI与对照) 和运动阶段被确定为主要预测因素.
结论:
- 优化的ELM模型可以准确地预测SCI患者在运动期间的温度调节反应.
- 在SCI组中,减少了外围温度调节的可变性.
- 这种预测能力可以帮助个性化的运动处方和SCI的管理.
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