A novel approach to predict energy of physical activity

描述(申请人提供)：体力活动(PA)在许多慢性病中的作用日益得到认可。准确而详细的PA测量是进一步探讨其与健康和疾病的关系的关键前提。小型可穿戴加速度计可以客观测量PA(PA计数)，同时还提供与PA相关的能量消耗的粗略估计(EEAcT)。然而，目前的功率放大器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功率放大器强度的关键特性--EEAC-r。为了从根本上指导未来PA监护仪的设计，以准确预测EEAcT，我们假设可以从多个身体节段的原始加速度和姿势信号中提取参数。利用精密仪器和专业技术的独特组合，我们建议开发一种新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24小时的全房间间接量热计和3小时自由生活期的便携式热量计来测量分钟到分钟的EEAcT。该测量的EEAcT将被用作预测模型的目标。我们将应用一种先进的建模技术(人工神经网络)对提取的PA参数进行建模，以达到对EEAcT的准确预测。将使用重复测量来交叉验证模型的预测精度。这项研究旨在涵盖肥胖、超重和瘦成人的不同人群样本(n=200)，并包括广泛的PA类型和强度。这项研究的意义在于，我们的研究结果将为开发下一代功放监测器提供洞察力，例如应该将传感器放置在身体的哪个位置，应该提取哪些信号参数，以及应该如何应用分析算法。此外，我们的研究将改进和验证EEAcT预测，通过几种市面上可用的PA监视器，从而为它们在该领域的应用提供立竿见影的好处。

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.