Occupant behavior monitoring and emergency event detection in single-person households using deep learning-based sound recognition
Occupant behavior monitoring and emergency event detection in single-person households using deep learning-based sound recognition
复制标题
DOI：
10.1016/j.buildenv.2020.107092
复制
发表时间：
2020-08-15
影响因子：
7.4
通讯作者：
Chi, Seokho
中科院分区：
文献类型：
--
作者：
Kim, Jinwoo;Min, Kyungjun;Chi, Seokho
由于各种社会问题，如死亡分离、结婚率下降和离婚率上升，单身家庭的数量一直在增加。不幸的是，这种人口变化正在产生一个新的社会问题，即孤独死亡。为了解决这个问题，许多研究人员试图开发基于传感器和计算机视觉的可穿戴系统，以监测居住者的行为并检测室内环境中可能的紧急事件。然而，现有方法由于其技术缺点而在监测SPH方面面临挑战;例如，如果乘员没有佩戴电子传感器，或者如果信号被其他物体遮挡，则不可能监测SPH。此外，由于现有的研究只集中在分类乘员的日常活动，如吃饭，坐着，说话，紧急事件，是显着的SPH监测仍然不清楚。为了应对这些挑战，本研究调查了对乘员健康有关键影响的紧急事件，并提出了一种基于深度学习的声音识别模型，以监控乘员行为并检测SPH环境中可能的紧急事件。实验进行了实际的SPH家庭环境和在线数据共享网站收集的音频数据。该模型的平均准确率和召回率分别为78.0%和90.8%。结果表明，该模型可以成功地区分紧急声音事件的正常人类活动的声音。该研究结果不仅可以对处于危险中的SPH进行安全保护和救援，而且为室内人员和事件监测提供了新的研究方向。
The number of single-person households (SPHs) has been consistently increasing owing to various social issues, such as separation by death, declining marriage rate, and increasing divorce rate. Unfortunately, this demographical change is creating a new social problem, namely, lonely death. In response to this problem, many researchers have attempted to develop wearable sensor-based and computer vision-based systems that monitor occupant behaviors and detect possible emergency events in indoor environments. However, existing approaches face challenges in monitoring SPHs owing to their technical disadvantages; for instance, if the occupant is not wearing the electronic sensor, or if the signal is occluded by other objects, it is not possible to monitor SPHs. Moreover, as existing studies focus only on classifying the occupant's daily activities, such as eating, sitting, and talking, the emergency events that are significant for SPH monitoring are still unclear. To address these challenges, this study investigates emergency events that have a critical impact on the occupant's health and proposes a deep learning-based sound recognition model to monitor occupant behaviors and detect possible emergency events in SPH environments. Experiments are conducted using audio data collected from actual SPH home environments and online data-sharing websites. The average precision and recall rates of the developed model are 78.0% and 90.8%, respectively. The results demonstrate that the developed model could successfully distinguish emergency sound events from the sounds of regular human activities. The findings can not only secure and rescue SPHs in danger but also provide new research directions for indoor occupant and event monitoring.