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Diet monitoring through breathing signal analysis using wearable sensors.

机译：使用可穿戴式传感器通过呼吸信号分析进行饮食监测。

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摘要

This dissertation presents a framework of wearable food and drink intake monitoring system that analyzes human breathing signal for identifying swallows during the intake process. The system works based on a key observation that a person's otherwise continuous breathing cycles are interrupted by brief intra-cycle apneas during the swallows. This dissertation develops wireless wearable electronics for capturing and processing human breathing signal, and algorithms for identifying intake-related swallows via recognizing apneas extracted from breathing signal. A family of apnea detection mechanisms including matched filters and machine learning has been developed. Algorithms are developed for detecting various types of swallowing events including for solid and liquid in the presence of many artifacts presents in free-living conditions. It is demonstrated that using these algorithms and the electronics, run-time intake monitoring and analysis are feasible at acceptable accuracy levels. Further accuracy improvements were explored using a Hidden Markov Model (HMM) based mechanism that leverages known temporal locality in the human swallow sequence. Finally, it was demonstrated that by combining swallowing signatures from breathing signal with hand movement signatures using accelerometers, it is possible to train a hierarchical Support Vector Machine (SVM) classifiers and a Hidden Markov Model (HMM) for accurate mealtime and duration estimation. The developed wearable system, along with a smartphone App, was experimentally validated on tens of subjects with approval from MSU's Institutional Review Board (IRB).
机译：本文提出了一种可穿戴食物和饮料摄入量监测系统的框架，该系统可以分析人的呼吸信号以识别摄入过程中的吞咽。该系统基于一个关键的观察结果而工作，即在吞咽过程中，人原本连续的呼吸周期被短暂的周期内呼吸暂停中断。本论文开发了用于捕获和处理人类呼吸信号的无线可穿戴电子设备，以及通过识别从呼吸信号中提取的呼吸暂停来识别与摄入有关的燕子的算法。已经开发出包括匹配过滤器和机器学习的呼吸暂停检测机制家族。开发了用于检测各种吞咽事件的算法，包括在自由生活条件下存在的许多伪影的情况下的固体和液体吞咽事件。结果表明，使用这些算法和电子设备，可以在可接受的精度水平下对运行时间进气进行监测和分析。使用基于隐马尔可夫模型（HMM）的机制探索了进一步的准确性，该机制利用了人类吞咽序列中的已知时间局部性。最后，证明了通过使用加速度计将呼吸信号中的吞咽信号与手部运动信号相结合，可以训练分层的支持向量机（SVM）分类器和隐马尔可夫模型（HMM），以准确地进餐和持续时间估算。经过MSU机构审查委员会（IRB）的批准，开发的可穿戴系统以及智能手机App已在数十个受试者上进行了实验验证。

著录项

  • 作者

    Dong, Bo.;

  • 作者单位

    Michigan State University.;

  • 授予单位 Michigan State University.;
  • 学科 Electrical engineering.;Computer engineering.;Computer science.;Biomedical engineering.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 149 p.
  • 总页数 149
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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