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

CSR：小：长期传感器网络部署的活动识别的现实性

• 批准号: 1319302
• 负责人: John Stankovic
• 金额: $ 42.33万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319302&HistoricalAwards=false
• 关键词: CSR; Small; Realism; Activity; Recognition

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.

无线传感器网络的研究在为许多应用领域（例如家庭保健，为建筑物中节省能源，基础设施监测，农业和各种环境科学应用程序）创建学术测试台和短期实际部署方面非常成功。但是，在不受控制的环境中长期部署出现了重大新问题。同样重要的是要注意，这些应用程序中的大多数都执行活动识别。但是，这些活动识别解决方案并不总是足够强大的长期部署。因此，这项工作的目标是为解决长期部署的现实主义的家庭部署开发可靠和可靠的活动识别。为了实现这一目标，需要新的研究结果：获取培训活动识别系统的标记地面真理，识别重叠的活动，检测到房屋房间跨房间发生的活动，处理缺失的传感器事件和传感器故障，解决多个人家庭和访客的问题，并处理人类行为的演变。这些解决方案必须以整体方式组合。此外，活动识别的实用性通常取决于识别典型人类行为的异常。异常检测也可能遭受长期部署的现实主义，因此也可以解决。基础研究方法包括以强大的方式采用数据挖掘，机器学习和其他技术，这些方法可以解释长期部署的现实主义。展示了从实验实验到现实的长期部署9个月或更长时间的实用性。这项工作的重要意义之所以发生，是因为为长期运行的无线传感器网络开发了可靠的活动识别方案，这意味着在家庭保健和房屋和建筑物中的能源和节省的能源等应用中的改善。家庭保健可以挽救生命，改善生活方式，并为老年人和慢性病，较低的医疗费用以及纵向研究提供更大的独立性，并增加对疾病原因的理解。能源是一种稀缺的资源，改进的活动识别可用于执行节省能量的控制动作。这种节能可以节省资金并降低全球变暖的影响。