Aziz Omar, Zihajehzadeh Shaghayegh, Park Aerin, Tae Chul-Gyu, Park Edward J
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3940-3944. doi: 10.1109/EMBC44109.2020.9176562.
Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. The challenge is to provide accurate EE estimations in free-living environment through portable and unobtrusive devices. In this paper, we present an experimental study to estimate energy expenditure during sitting, standing and treadmill walking using a smartwatch. We introduce a novel methodology, which aims to improve the EE estimation by first separating sedentary (sitting and standing) and non-sedentary (walking) activities, followed by estimating the walking speeds and then calculating the energy expenditure using advanced machine learning based regression models. Ten young adults participated in the experimental trials. Our results showed that combining activity type and walking speed information with the acceleration counts substantially improved the accuracy of regression models for estimating EE. On average, the activity-based models provided 7% better EE estimation than the traditional acceleration-based models.
能量消耗（EE）估计是追踪个人活动和预防慢性疾病（如肥胖症和糖尿病）的一个重要因素。面临的挑战是通过便携式且不引人注意的设备在自由生活环境中提供准确的EE估计。在本文中，我们展示了一项使用智能手表估计坐姿、站姿和跑步机行走过程中能量消耗的实验研究。我们引入了一种新颖的方法，该方法旨在通过首先分离久坐（坐姿和站姿）和非久坐（行走）活动，接着估计行走速度，然后使用基于先进机器学习的回归模型计算能量消耗来提高EE估计。十名年轻成年人参与了实验试验。我们的结果表明，将活动类型和行走速度信息与加速度计读数相结合，显著提高了用于估计EE的回归模型的准确性。基于活动的模型平均比传统的基于加速度的模型提供了7%更好的EE估计。
