Augustyniak Piotr, Smoleń Magdalena, Mikrut Zbigniew, Kańtoch Eliasz
AGH-University of Science and Technology, 30, Mickiewicz Ave., 30-059 Kraków, Poland.
Sensors (Basel). 2014 Apr 29;14(5):7831-56. doi: 10.3390/s140507831.
This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system.
本文提出了一种用于在老年人生活环境中进行无缝监测的多模态系统。该系统同时使用针对每个个体的可穿戴传感器网络以及针对每个环境的嵌入式传感器。本文展示了利用来自两种类型移动传感器的互补信息的优势：基于视觉流的图像分析和基于加速度计的可穿戴网络。本文给出了室内几种基本姿势识别以及室外复杂动作识别的结果。本文没有对整个系统进行描述，而是特别关注了基于极坐标直方图的视觉姿势识别方法、可穿戴网络和嵌入式网络数据的互补使用与同步，以及由两个与场所和主体相关的数据库驱动的自动危险检测算法。我们方法的新颖之处还在于用来自主体的真实生活记录填充数据库，以及使用动态时间规整算法来测量行为记录中表示为基本姿势的动作之间的距离。测试我们方法的主要结果包括：视频系统对基本姿势识别的准确率为95.5%，基于加速度计的系统对基本姿势识别的准确率为96.7%，基于加速度计和视频的组合系统对基本姿势识别的准确率为98.9%，基于加速度计的可穿戴系统对复杂室外活动识别的准确率为80%。
