Doukas Charalampos N, Maglogiannis Ilias
Department of Information and CommunicationSystems Engineering, University of the Aegean, Mytilene, Greece.
IEEE Trans Inf Technol Biomed. 2011 Mar;15(2):277-89. doi: 10.1109/TITB.2010.2091140. Epub 2010 Nov 9.
This paper presents the implementation details of a patient status awareness enabling human activity interpretation and emergency detection in cases, where the personal health is threatened like elder falls or patient collapses. The proposed system utilizes video, audio, and motion data captured from the patient's body using appropriate body sensors and the surrounding environment, using overhead cameras and microphone arrays. Appropriate tracking techniques are applied to the visual perceptual component enabling the trajectory tracking of persons, while proper audio data processing and sound directionality analysis in conjunction to motion information and subject's visual location can verify fall and indicate an emergency event. The postfall visual and motion behavior of the subject, which indicates the severity of the fall (e.g., if the person remains unconscious or patient recovers) is performed through a semantic representation of the patient's status, context and rules-based evaluation, and advanced classification. A number of advanced classification techniques have been examined in the framework of this study and their corresponding performance in terms of accuracy and efficiency in detecting an emergency situation has been thoroughly assessed.
本文介绍了一种患者状态感知系统的实现细节，该系统能够在个人健康受到威胁的情况下（如老年人跌倒或患者晕倒）进行人类活动解读和紧急情况检测。所提出的系统利用通过适当的身体传感器从患者身体以及使用高架摄像机和麦克风阵列从周围环境中捕获的视频、音频和运动数据。适当的跟踪技术应用于视觉感知组件，以实现人员轨迹跟踪，同时结合运动信息和对象的视觉位置进行适当的音频数据处理和声音方向性分析，可以验证跌倒并指示紧急事件。通过对患者状态、上下文和基于规则的评估进行语义表示以及先进的分类，来执行受试者跌倒后的视觉和运动行为，以表明跌倒的严重程度（例如，此人是否仍昏迷或患者是否恢复）。在本研究框架内研究了多种先进的分类技术，并对它们在检测紧急情况时的准确性和效率方面的相应性能进行了全面评估。
