Department of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China.
School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel). 2020 Jul 19;20(14):4016. doi: 10.3390/s20144016.
As the foundation of Posture Analysis, recognizing human activity accurately in real time assists in using machines to intellectualize living condition and monitor health status. In this paper, we focus on recognition based on raw time series data, which are continuously sampled by wearable sensors, and a fine-grained evidence reasoning approach has been proposed to produce a timely and reliable result. First, the basic time unit of input data is selected by finding a tradeoff between accuracy and time cost. Then, the approach uses Long Short Term Memory to extract features and project raw multidimensional data into probability assignments, followed by trainable evidence combination and inference network that reduce uncertainly to improve the classification accuracy. Experiments validate the effectiveness of fine granularity and evidence reasoning while the final results indicate that the recognition accuracy of this approach can reach 96.4% with no additional complexity in training.
作为姿势分析的基础，准确实时地识别人类活动有助于机器智能化生活条件和监测健康状况。在本文中，我们专注于基于原始时间序列数据的识别，这些数据由可穿戴传感器连续采样，并提出了一种细粒度的证据推理方法来生成及时可靠的结果。首先，通过在准确性和时间成本之间找到折衷方案来选择输入数据的基本时间单位。然后，该方法使用长短期记忆来提取特征，并将原始多维数据投影到概率分配中，然后是可训练的证据组合和推理网络，以降低不确定性，提高分类准确性。实验验证了细粒度和证据推理的有效性，最终结果表明，该方法的识别准确率可达 96.4%，而训练复杂度没有增加。
