NeTS-NOSS: Collaborative Research: The BehaviorScope Project: Sensory Grammars for Sensor Networks

NeTS-NOSS：协作研究：BehaviorScope 项目：传感器网络的感觉语法

• 批准号: 0721632
• 负责人: Andreas Savvides
• 金额: $ 15万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721632&HistoricalAwards=false
• 关键词: NeTS; NOSS; Collaborative; Research; BehaviorScope

The BehaviorScope project seeks to develop a framework for understanding patterns and behaviors from sensor data and metadata in distributed multimodal sensor nodes. Patterns and behaviors (especially of humans) will be parsed by a hierarchy of probabilistic grammars and other mechanisms into a compact and more descriptive semantic form. These higher-level interpretations of the data will provide the necessary network cognition needed to provide services in many everyday life applications such as assisted living, workplace safety, security, entertainment and more. The project will use a lightweight camera sensor network as its primary platform and will focus on two types of spatio-temporal data processing. At the local sensor's field of view, this research will investigate the design of filters for robustly detecting humans as well as their gestures and postures. At a more macroscopic level, collections of sensors will coordinate to detect longer term patterns of behavior. The expected outcome is a new data interpretation framework that can understand the spatial and temporal aspects of data and respond to them with meaningful services. To collect real data and to demonstrate the developed concepts in practical applications, this work will use assisted living as the driver application. In this context, the developed sensor network will supervise the behaviors of elders living alone at home to generate daily activity summaries, post warnings and alarms when they engage in dangerous activities, and provide a variety of services that increase the autonomy and independence of these individuals.

BehaviorScope项目寻求开发一个框架，用于从分布式多模式传感器节点的传感器数据和元数据中理解模式和行为。模式和行为(尤其是人类的模式和行为)将由概率语法和其他机制的层次结构解析为紧凑和更具描述性的语义形式。这些对数据的更高级别的解释将提供必要的网络认知，以便在许多日常生活应用中提供服务，如辅助生活、工作场所安全、安全、娱乐等。该项目将使用一个轻型相机传感器网络作为其主要平台，并将专注于两种类型的时空数据处理。在当地传感器的视野内，这项研究将调查用于稳健检测人类及其手势和姿势的过滤器的设计。在更宏观的层面上，传感器集合将相互协调，以检测更长期的行为模式。预期成果是一个新的数据解释框架，它可以理解数据的空间和时间方面，并以有意义的服务对其作出回应。为了收集真实的数据并在实际应用中展示所开发的概念，本工作将使用辅助生活作为驾驶员应用程序。在此背景下，开发的传感器网络将监督独居老人的行为，以生成日常活动摘要，当他们从事危险活动时发布警告和警报，并提供各种服务，增加这些个人的自主性和独立性。