CSR: Small: Realism in Activity Recognition for Long Term Sensor Network Deployments

无线传感器网络的研究已经非常成功地为许多应用领域创建了学术测试平台和短期真实的部署，例如家庭医疗保健、建筑节能、基础设施监测、农业和各种环境科学应用。然而，在不受控制的环境中长期部署会出现重大的新问题。同样重要的是要注意，这些应用程序中的大多数执行活动识别。然而，这些活动识别解决方案对于长期部署来说并不总是足够强大。因此，这项工作的目标是为家庭部署开发强大而可靠的活动识别，以解决长期部署的现实问题。为了实现这一目标，需要在以下方面取得新的研究成果：获得用于训练活动识别系统的标记地面实况，识别重叠活动，检测家庭房间中发生的活动，处理缺失的传感器事件和传感器故障，解决多人家庭和访客的问题，以及处理人类行为的演变。这些解决办法必须以整体方式结合起来。此外，活动识别的效用通常取决于从典型的人类行为中识别异常。异常检测也可能受到长期部署的现实影响，因此也得到了解决。基础研究方法包括以稳健的方式采用数据挖掘，机器学习和其他技术，以解决长期部署中的现实问题。该解决方案的实用性的演示跨越从实验室实验到现实的长期部署为9个月或更长的时间，这项工作的广泛意义，因为发展强大的活动识别方案的无线传感器网络，运行了很长一段时间，意味着改善应用，如家庭医疗保健和节约能源的家庭和建筑物。家庭保健可以挽救生命，为老年人和慢性病患者提供更好的生活方式和更大的独立性，降低医疗成本，并通过纵向研究增加对疾病原因的了解。能量是稀缺资源，并且改进的活动识别可以用于执行节省能量的控制动作。这种节能可以节省资金，降低全球变暖的影响。

Research in wireless sensor networks has been very successful in creating academic testbeds and short term real deployments for many application areas such as home health care, saving energy in buildings, infrastructure monitoring, agriculture, and various environmental science applications. However, significant new problems arise for long term deployments in uncontrolled environments. It is also important to note that most of these applications perform activity recognition. Yet, these activity recognition solutions are not always robust enough for long term deployments. Consequently, the goal of this work is to develop robust and reliable activity recognition for in-home deployments that address the realism of long term deployments. To accomplish this goal requires new research results in: obtaining labeled ground truth for training activity recognition systems, recognizing overlapping activities, detection of activities that occur across rooms of a home, handling missing sensor events and sensor failures, addressing the issues of multiple person homes and visitors, and handling the evolution of human behaviors. These solutions must be combined in a holistic manner. In addition, the utility of activity recognition often depends on recognizing anomalies from typical human behaviors. Anomaly detection can also suffer from the realisms of long term deployments and, therefore, is also addressed. The basic research approach includes employing data mining, machine learning, and other techniques in robust ways that account for realisms in long term deployments. Demonstration of the utility of the solutions spans from lab experiments to realistic long term deployments for 9 months or longer.The broad significance of this work occurs because developing robust activity recognition schemes for wireless sensor networks that operate for long time periods implies improvement in applications such as home health care and saving energy in homes and buildings. Home health care can save lives, provide improved life style and greater independence for the elderly and chronically ill, lower medical costs, and via longitudinal studies, increase understanding of the causes of diseases. Energy is a scarce resource and improved activity recognition can be used to perform control actions that save energy. This energy savings can save money and lower the impact of global warming.