Li Zhen, Wei Zhiqiang, Huang Lei, Zhang Shugang, Nie Jie
College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2016 Oct 15;16(10):1713. doi: 10.3390/s16101713.
Human activity recognition is important for healthcare and lifestyle evaluation. In this paper, a novel method for activity recognition by jointly considering motion sensor data recorded by wearable smart watches and image data captured by RGB-Depth (RGB-D) cameras is presented. A normalized cross correlation based mapping method is implemented to establish association between motion sensor data with corresponding image data from the same person in multi-person situations. Further, to improve the performance and accuracy of recognition, a hierarchical structure embedded with an automatic group selection method is proposed. Through this method, if the number of activities to be classified is changed, the structure will be changed correspondingly without interaction. Our comparative experiments against the single data source and single layer methods have shown that our method is more accurate and robust.
人类活动识别对于医疗保健和生活方式评估至关重要。本文提出了一种新颖的活动识别方法，该方法通过联合考虑可穿戴智能手表记录的运动传感器数据和RGB深度（RGB-D）相机捕获的图像数据来实现。实现了一种基于归一化互相关的映射方法，以在多人场景中建立来自同一人的运动传感器数据与相应图像数据之间的关联。此外，为了提高识别的性能和准确性，提出了一种嵌入自动组选择方法的分层结构。通过这种方法，如果要分类的活动数量发生变化，结构将相应地改变而无需交互。我们针对单数据源和单层方法的对比实验表明，我们的方法更准确、更稳健。
