可穿戴传感器的人体活动能量预测模型研究  

Research on Prediction Model of Physical Activity Energy Expenditure with Wearable Sensors

作　　者：王琳[1] 孙倩 马晓娜[1] 高永艳[2] 刘毅[1] 马宏伟[1] 杨东强 WANG Lin;SUN Qian;MA Xiaona;GAO Yongyan;LIU Yi;MA Hongwei;YANG Dongqiang(College of Computer Science and Technology,Shandong Jianzhu University,Jinan 250101,China;Department of Sports,Shandong Jianzhu University,Jinan 250101,China)

机构地区：[1]山东建筑大学计算机科学与技术学院,济南250101 [2]山东建筑大学体育部,济南250101

出　　处：《计算机科学与探索》2022年第12期2832-2840,共9页Journal of Frontiers of Computer Science and Technology

基　　金：山东省自然科学基金(ZR2018MF012,ZR2020QF029);山东建筑大学博士基金(XNBS1811)。

摘　　要：为了解决可穿戴传感器特征过多与嵌入式设备计算能力和存储能力有限的矛盾问题,在多传感器(加速度传感器、陀螺仪传感器)数据融合的基础上,采用特征工程的方法选出人体运动能量消耗预测(PAEE)的最优特征。在数据预处理阶段,使用滑动窗口技术提取传感器的时域、频域特征,对三个速度水平的数据集使用正弦曲线拟合,并通过显著性差异检验分析选出有效数据。构建了过滤式、封装式和嵌入式特征选择算法与多线性回归、回归树、支持向量机和神经网络等机器学习预测模型结合的WEKA实验平台。最后决策级融合时,通过评估每个模型的相关系数和平均绝对误差选择出最优模型。模型训练时采用带抖动的数据集作为测试集,避免出现模型的过拟合现象,提高模型的泛化能力和鲁棒性。嵌入式特征选择采用经典的弹性网络算法。实验结果表明,在PAEE中加速度计传感器的特征比陀螺仪传感器的特征更具有决定性的作用,基于相关系数方法的多传感器特征融合的神经网络模型是最优模型。To solve the contradiction between multiple wearable sensor features and the limited computing power and storage capacity of embedded devices,feature engineering is used to select the best features for predicting physical activity energy expenditure(PAEE)on the basis of data fusion of multiple sensors(accelerometer and gyroscope sensors).In the data preprocessing stage,time-domain and frequency-domain features of the sensor are extracted by using sliding window technology,and sinusoidal curve fitting is used for dataset at three velocity levels,finally hypothesis testing is carried out to check data outliers.A WEKA experimental platform is constructed based on filtering,warpper and embedded feature selection algorithms and machine learning prediction models such as multiple linear regression,regression tree,support vector machine and neural network.Finally,the optimal model is selected by evaluating the correlation coefficient and mean absolute error of each model during the decision level fusion.The dataset with jitter is used as the test data,which shows that feature selection can mitigate model overfitting and improve the model’s generalization ability and robustness.Embedded feature selection adopts classical elastic network algorithm.Experimental results show that the features extracted from accelerometer sensors play a more decisive role than those from gyroscope sensors in PAEE and the neural network model of multi-sensor feature fusion based on correlation coefficient method is the optimal model.

关 键 词：人体活动能量消耗 特征选择 神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]