Tasoulis S K, Doukas C N, Maglogiannis I, Plagianakos V P
Department of Computer Science and Biomedical Informatics, University of Central Greece, Papassiopoulou 2–4, Lamia 35100, Greece.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:3720-3. doi: 10.1109/IEMBS.2011.6090632.
The analysis of human motion data is interesting for the purpose of activity recognition or emergency event detection, especially in the case of elderly or disabled people living independently in their homes. Several techniques have been proposed for identifying such distress situations using either motion, audio or video sensors on the monitored subject (wearable sensors) or the surrounding environment. The output of such sensors is data streams that require real time recognition, especially in emergency situations, thus traditional classification approaches may not be applicable for immediate alarm triggering or fall prevention. This paper presents a statistical mining methodology that may be used for the specific problem of real time fall detection. Visual data captured from the user's environment, using overhead cameras along with motion data are collected from accelerometers on the subject's body and are fed to the fall detection system. The paper includes the details of the stream data mining methodology incorporated in the system along with an initial evaluation of the achieved accuracy in detecting falls.
出于活动识别或紧急事件检测的目的，对人体运动数据进行分析很有意思，尤其是对于那些独立居住在家中的老年人或残疾人而言。已经提出了几种技术，用于使用监测对象（可穿戴传感器）或周围环境中的运动、音频或视频传感器来识别此类遇险情况。此类传感器的输出是需要实时识别的数据流，尤其是在紧急情况下，因此传统的分类方法可能不适用于立即触发警报或预防跌倒。本文提出了一种统计挖掘方法，可用于实时跌倒检测的特定问题。从用户环境中使用高架摄像机捕获的视觉数据，连同运动数据一起，从受试者身体上的加速度计收集，并输入到跌倒检测系统中。本文包括系统中所采用的流数据挖掘方法的详细信息，以及对跌倒检测所达到的准确率的初步评估。
