A novel approach to predict energy of physical activity

预测身体活动能量的新方法

• 批准号: 7094100
• 负责人: MACIEJ S BUCHOWSKI
• 金额: $ 32.66万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7094100
• 关键词: analytical method; bioenergetics; bioengineering /biomedical engineering; biomedical equipment development; biosensor device; body movement; body physical activity; body weight; calorimetry; chordate locomotion; clinical research; human subject; measurement; miniature biomedical equipment; monitoring device; portable biomedical equipment

DESCRIPTION (provided by applicant): The role of physical activity (PA) in many chronic diseases is increasingly being recognized. The accurate and detailed measurement of PA is a crucial prerequisite to further explore its association with health and disease. Small and wearable accelerometers allow objective measurement of PA (PA counts), while also providing a rough estimation of the energy expenditure associated with PA (EEAcT). However, current PA monitors are restricted to using time-averaged signals and linear regression algorithms which consistently provide inaccurate predictions of EEAc-r- the key characteristic of PA intensity. To fundamentally guide the future designs of PA monitors to accurately predict EEAcT, we hypothesize that parameters can be extracted from the raw acceleration and postural signals of multiple body segments. Using a unique combination of sophisticated instruments and technical expertise, we propose to develop a novel analytical approach for accurately predicting EEAc-reutilizing the raw acceleration signals from upper and lower body segments. This is accomplished by continuously measuring movement and postures, at a rate of 32 samples/second, using a custom-designed monitor that consists of an array of 10 accelerometers. We will measure minute-to minute EEAcT using a whole-room indirect calorimeter for a 24-hour period, and a portable calorimeter for a 3-hour free-living period. This measured EEAcT will be used as the target for the prediction model. We will apply an advanced modeling technique (artificial neural networks) to model the extracted PA parameters to arrive at an accurate prediction of EEAcT. Repeated measurements will be used to cross-validate the prediction accuracy of the model. The study is designed to encompass a heterogeneous population sample (n=200) of obese, overweight, and lean adults, and to include a wide range of PA types and intensities. The significance of this research is that our results will provide insight for developing the next-generation PA monitors, such as where on the body the sensors should be placed, what signal parameters should be extracted, and how the analytical algorithms should be applied. In addition, our study will improve and validate EEAcT prediction by several market-available PA monitors, thus offering immediate benefits to their applications in the field.

描述（由申请人提供）：体力活动 (PA) 在许多慢性疾病中的作用日益得到认识。准确、详细的 PA 测量是进一步探讨 PA 与健康和疾病关系的关键先决条件。小型可穿戴加速度计可以客观测量 PA（PA 计数），同时还可以粗略估计与 PA 相关的能量消耗（EEAcT）。然而，当前的 PA 监测仪仅限于使用时间平均信号和线性回归算法，这些算法始终无法准确预测 EEAc-r（PA 强度的关键特征）。为了从根本上指导 PA 监视器的未来设计以准确预测 EEAcT，我们假设可以从多个身体部位的原始加速度和姿势信号中提取参数。通过将先进的仪器和技术专业知识独特地结合起来，我们建议开发一种新颖的分析方法来准确预测 EEAc——重新利用来自上半身和下半身的原始加速度信号。这是通过使用由 10 个加速度计阵列组成的定制设计的监视器以每秒 32 个样本的速率连续测量运动和姿势来实现的。我们将使用全房间间接热量计测量 24 小时的每分钟 EEAcT，并使用便携式热量计测量 3 小时的自由生活时间。测得的 EEAcT 将用作预测模型的目标。我们将应用先进的建模技术（人工神经网络）对提取的 PA 参数进行建模，以准确预测 EEAcT。重复测量将用于交叉验证模型的预测准确性。该研究旨在涵盖肥胖、超重和瘦成年人的异质人群样本 (n=200)，并包括广泛的 PA 类型和强度。这项研究的意义在于，我们的结果将为开发下一代 PA 监视器提供见解，例如传感器应放置在身体的何处、应提取哪些信号参数以及应如何应用分析算法。此外，我们的研究将改进和验证几种市场上可用的 PA 监视器的 EEAcT 预测，从而为其在该领域的应用提供直接的好处。