Assessing activity intensity has its clinical importance to the treatment of diseases such as obesity. The metabolic equivalent of task (MET) is the objective numerical measure for assessing the intensity of general activities. Because daily activities vary, an activity cannot be easily mapped to a MET value, which makes intensity quantification of daily activities more challenging than monotonous activities. In this article, we use the data from wearable inertial measurement unit (IMU) sensors and a calorimetry machine to map the relationship between activity motions and intensities. In detail, we describe an end-to-end approach for predicting METs of preschoolers. Based on the collection of data from the two devices, we present a systematic approach to address the aforementioned challenges and to predict physical activity intensity of preschoolers. Specifically, a dynamic synchronization method is first proposed to deal with the displaced data series, which takes the dynamic time warping (DTW) as an evaluation criterion. Second, additional features are designed to reinforce the ability of intensity prediction. Third, proposed methods are tested on a two-layer bidirectional long short term memory (LSTM) network model to predict MET values. Our experimental results reveal the effectiveness of the end-to-end approach.

评估活动强度对于治疗肥胖症等疾病具有重要的临床意义。任务代谢当量 (MET) 是用于评估一般活动强度的客观数值度量。由于日常活动各不相同，因此无法轻松将活动映射到 MET 值，这使得日常活动的强度量化比单调活动更具挑战性。在本文中，我们使用来自可穿戴惯性测量单元 (IMU) 传感器和量热仪的数据来绘制活动运动与强度之间的关系。详细地，我们描述了一种用于预测学龄前儿童 MET 的端到端方法。根据从两个设备收集的数据，我们提出了一种系统的方法来应对上述挑战并预测学龄前儿童的身体活动强度。具体而言，首先提出了一种动态同步方法来处理位移数据序列，该方法以动态时间扭曲（DTW）为评价标准。其次，设计附加功能以增强强度预测能力。第三，所提出的方法在两层双向长短期记忆 (LSTM) 网络模型上进行了测试，以预测 MET 值。我们的实验结果揭示了端到端方法的有效性。附加功能旨在加强强度预测的能力。第三，所提出的方法在两层双向长短期记忆 (LSTM) 网络模型上进行了测试，以预测 MET 值。我们的实验结果揭示了端到端方法的有效性。附加功能旨在加强强度预测的能力。第三，所提出的方法在两层双向长短期记忆 (LSTM) 网络模型上进行了测试，以预测 MET 值。我们的实验结果揭示了端到端方法的有效性。