利用智能手机从人体步态预测生理发育 传感器数据

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

摘要翻译：
冠状动脉疾病、心力衰竭、心绞痛和糖尿病是全球发病率和死亡率的主要原因。生活方式的改变、不良的饮食习惯、衰老和肥胖加剧了这种疾病的易感性。此外，传统的诊断方法在早期发现这种病理的能力是有限的。这就产生了对自动推荐系统的需求，该系统可以有效地监测和预测体内的致病行为。为此，我们提出了人体步态分析来预测与不同疾病相关的两个重要生理参数：体重指数和年龄。通过主动分析步态样本来预测年龄和体重指数，可以进一步用于提供合适的保健建议。然而，现有的预测年龄和体重指数的策略需要严格的实验环境来实现适当的性能。例如，精确记录的语音信号最近被用于预测不同受试者的体重指数。同样，通过记录来自人体和可穿戴传感器的步态样本来预测年龄组。这种专门的方法限制了对人类年龄和体重指数的主动和方便的分析。我们通过引入智能手机传感器作为记录步态信号的手段来解决这些问题。使用车载加速度计和陀螺仪有助于开发易于使用的预测体重指数和年龄的系统。为了实证证明我们提出的方法的有效性，我们从63个不同的受试者中收集了步态样本，这些受试者使用六个著名的机器学习分类器按体重指数和年龄组进行分类。我们使用两种不同的窗口操作来进行特征提取，即高斯和平方。
---
英文标题：
《Predicting physiological developments from human gait using smartphone
  sensor data》
---
作者：
Umair Ahmed, Muhammad Faizyab Ali, Kashif Javed, Haroon Atique Babri
---
最新提交年份：
2017
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
--

---
英文摘要：
  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.
---
PDF链接：
https://arxiv.org/pdf/1712.07958

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：智能手机 传感器 Applications Optimization Experimental body different 使用 human 预测