[论文解读] Predicting physiological developments from human gait using smartphone sensor data

Umair Ahmed, Muhammad Faizyab Ali+2 more|arXiv (Cornell University)|Dec 13, 2017

Context-Aware Activity Recognition Systems参考文献 23被引用 4

一句话总结

本研究提出一种基于智能手机的系统，通过人体步态的加速度计和陀螺仪数据，利用手工特征与高斯窗和方窗窗函数进行特征提取，结合机器学习方法预测身体质量指数（BMI）和年龄组。表现最佳的模型为k-最近邻（KNN），在使用高斯滤波时，BMI预测的真正率（true positive rate）达到94.47%，年龄分类的真正率达到95.9%。

ABSTRACT

Coronary artery disease, heart failure, angina pectoris and diabetes are among the leading causes of morbidity and mortality over the globe. Susceptibility to such disorders is compounded by changing lifestyles, poor dietary routines, aging and obesity. Besides, conventional diagnostics are limited in their capability to detect such pathologies at an early stage. This generates demand for automatic recommender systems that could effectively monitor and predict pathogenic behaviors in the body. To this end, we propose human gait analysis for predicting two important physiological parameters associated with different diseases, body mass index and age. Predicting age and body mass index by actively profiling the gait samples, could be further used for providing suitable healthcare recommendations. Existing strategies for predicting age and body mass index, however, necessitate stringent experimental settings for achieving appropriate performance. For instance, precisely recorded speech signals were recently used for predicting body mass indices of different subjects. Similarly, age groups were predicted by recording gait samples from on-body and wearable sensors. Such specialized methods limit active and convenient profiling of human age and body mass indices. We address these issues, by introducing smartphone sensors as a means for recording gait signals. Using on-board accelerometer and gyroscope helps in developing easy-to-use and accessible systems for predicting body mass index and age. To empirically show the effectiveness of our proposed methodology, we collected gait samples from sixty-three different subjects that were classified in body mass index and age groups using six well-known machine learning classifiers. We evaluated our system using two different windowing operations for feature extraction, namely Gaussian and Square.

研究动机与目标

• 开发一种可访问、非侵入性的方法，利用智能手机传感器预测BMI等生理参数。
• 克服现有方法依赖专用设备、语音信号或可穿戴传感器的局限性。
• 通过持续的基于步态的监测，实现糖尿病、冠状动脉疾病和心力衰竭等疾病的早期检测。
• 评估不同窗函数技术（高斯窗与方窗）在提升BMI和年龄预测分类准确率方面的有效性。
• 识别用于基于智能手机数据的步态生理预测的最优机器学习分类器及特征提取参数。

提出的方法

• 在正常步行过程中，从63名受试者收集智能手机加速度计和陀螺仪传感器的步态数据。
• 通过两种窗函数操作（高斯窗与方窗）从分段的时间序列数据中提取14个统计特征。
• 采用10折交叉验证以确保分类器性能的统计显著性。
• 使用六种机器学习分类器：KNN、随机森林、支持向量机（SVM）、多层感知机、J48决策树和逻辑回归。
• 通过实证评估优化窗函数参数（BMI的σ = 0.36s，年龄的σ = 0.5s；方窗宽度分别为0.83s和1.11s）。
• 进行频域分析，解释为何高斯滤波优于方窗滤波，原因在于减少频谱泄漏和噪声放大。

实验结果

研究问题

• RQ1 在无需专用传感器或受控环境的条件下，基于智能手机的步态分析能否准确预测BMI和年龄组？
• RQ2 不同窗函数技术（高斯窗与方窗）如何影响机器学习模型在从步态信号中分类BMI和年龄时的性能？
• RQ3 哪种机器学习分类器在基于智能手机传感器数据预测BMI和年龄时准确率最高？
• RQ4 在基于步态的生理参数预测中，特征提取的最优窗函数大小和标准差是什么？
• RQ5 为何高斯滤波在分类准确率方面优于方窗滤波？其背后的信号处理原理是什么？

主要发现

• k-最近邻（KNN）在使用σ = 0.36s的高斯窗函数时，BMI预测的真正率最高，达到94.47%。
• 在年龄分类任务中，KNN在σ = 0.5s的高斯滤波下取得95.9%的真正率，优于其他分类器。
• 支持向量机（SVM）在使用宽度为1.11s的方窗时，年龄预测准确率达到94.28%，在所有模型中排名第二。
• 当高斯滤波的标准差超过阈值（BMI为0.31s，年龄为0.56s）时，其性能显著提升，原因在于更集中的频响特性。
• 傅里叶分析表明，方窗窗函数会产生频谱旁 lob（Sinc响应），增加对噪声的敏感性，而高斯窗则收敛为单一主瓣，减少干扰。
• 逻辑回归在两项任务中表现均较差，BMI和年龄预测的真正率分别仅为69.43%和69.04%。