论文标题
通过使用智能手机通过日常活动识别的能源支出估算
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone
论文作者
论文摘要
本文提出了一个三步系统，该系统以非侵入性的方式估算个人的实时能量消耗。首先，使用用户的智能手机的传感器，我们构建了一个决策树模型来识别其体育锻炼（\ textit {running}，\ textit {steration}，...）。然后，我们使用检测到的体育活动，时间和用户的速度来推断他的日常活动（\ textit {看电视}，\ textit {上浴室}，...），通过使用加固学习环境，部分可观察到的马尔可夫决策过程框架。一旦确认了日常活动，我们将这些信息转化为使用体育锻炼纲要的能量支出。通过成功检测到90 \％的8种体育活动，我们在识别17种不同的日常活动时达到了80 \％的总体准确性。该结果使我们以平均误差为预期估计的26 \％估算用户的能量消耗。
This paper presents a 3-step system that estimates the real-time energy expenditure of an individual in a non-intrusive way. First, using the user's smart-phone's sensors, we build a Decision Tree model to recognize his physical activity (\textit{running}, \textit{standing}, ...). Then, we use the detected physical activity, the time and the user's speed to infer his daily activity (\textit{watching TV}, \textit{going to the bathroom}, ...) through the use of a reinforcement learning environment, the Partially Observable Markov Decision Process framework. Once the daily activities are recognized, we translate this information into energy expenditure using the compendium of physical activities. By successfully detecting 8 physical activities at 90\%, we reached an overall accuracy of 80\% in recognizing 17 different daily activities. This result leads us to estimate the energy expenditure of the user with a mean error of 26\% of the expected estimation.
