基于运动量的神经网络心率预测器的设计及对比研究

目的 利用神经网络建立有效的基于运动量的心率预测模型,分析运动量与心率变化之间的关系.方法 通过对运动量信号进行不同分析（预处理）,并采用不同的神经网络的结构及学习算法,单步或多步预测方式建立了6个预测模型,然后利用采集到的真实数据进行测试,并对各模型结构框架及预测结果进行了对比.结果 建立的模型平均预测误差均保持在一个很小的范围内.结论 利用神经网络建立心率预测模型可有效地反映运动量如何影响心率变化.对比结果表明,在单步预测中,利用神经网络拓扑增强技术（neuro-evolution of augmenting topologies,NEAT）建立的心率预测模型可达到最佳的预测效果,而多步预测利用Adams-Bashforth技术得到的预测结果是最好的.

Design and comparison research of heart rate prediction model based on physical activities using neural network

Objective To find the relationship between physical activitie.s （PA） and HR change by building an effective PA-based HR prediction model. Methods Six models were built according to different PA signal analysis scheme （preprocessing）, different NN structures and training algorithms, and different prediction steps （single-step or multi-step）. Then the models were tested by using the data collected from real life and comparisons were made between the results. Results The average prediction errors of the models were restricted in a small range. Conclusions The experimental results demonstrated that models built by NN could effectively reflect how PA affect HR, and the comparison results illustrated that the model built by neuroevolution of augmenting topologies （NEAT） got the best performance in single-step prediction. And Adams- Bashforth technique was the best choice in multi-step prediction.