IEEE J Transl Eng Health Med. 2015 Sep 18;3:2700212. doi: 10.1109/JTEHM.2015.2480082. eCollection 2015.
Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings.
能量消耗（EE）估计是跟踪个人活动和预防肥胖症和糖尿病等慢性病的一个重要因素。利用小型可穿戴传感器进行准确和实时的 EE 估计是一项艰巨的任务，主要是因为大多数现有方案是离线工作或使用启发式方法。在本文中，我们专注于准确估计智能手机用户日常活动（步行、站立、爬楼上或下楼）的 EE。我们使用内置智能手机传感器（加速度计和气压计传感器）以低频率采样，以准确估计 EE。除了加速度计传感器外，使用气压计传感器可大大提高 EE 估计的准确性。我们使用袋装回归树，一种机器学习技术，为 EE 估计开发了一个通用的回归模型，其与实际 EE 的相关性高达 96％。我们将结果与最先进的量热法方程和消费类电子产品（Fitbit 和 Nike+ FuelBand）进行了比较。与当前可用的方法相比，新开发的 EE 估计算法具有更高的准确性。结果经过 COSMED K4b2 量热计读数校准。
