Context-Aware Data Analytics for Sensor Arrays

传感器阵列的上下文感知数据分析

• 批准号: RGPIN-2014-04381
• 负责人: Goubran, Rafik
• 金额: $ 2.26万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637465
• 关键词: Context; Aware; Data; Analytics; Sensor

The main objective of this research program is to design smart context-aware data processing algorithms to extract useful information from large data streams. We will use context information from the environment to improve the system’s performance.During the past few years, advances in the areas of semiconductors and micro electro mechanical systems (MEMS) led to the introduction of new compact sensors such as pressure sensors, accelerometers, gyroscopes, microphone arrays and electronic noses. These advances, coupled with powerful processors and embedded wireless interfaces enabled the introduction of smart sensor systems with minimal power consumption.This project will propose novel noise cancellation, feature extraction, pattern recognition and classification approaches to extract useful information from multiple sensors. These sensors must work synergistically to provide the user with the required information at the right time, and in the right format given a specific context. The project will consider fixed sensors and smart-phone-based sensors for two applications: the design of smart homes and audio signal processing. Fixed sensors are usually deployed in a given environment such as a smart home or critical infrastructure. In this program, we will investigate several sensor technologies, in particular pressure-sensitive arrays and microphone arrays. Pressure-sensitive arrays measure the pressure applied at any point on their surface. When embedded into floors, these sensors are capable of detecting intruders, or performing gait analysis for biometric purposes or health diagnosis. When placed under a bed mattress, advanced signal processing algorithms can be used to extract bed occupancy, breathing, or bed exit characteristics (timing, symmetry, bouncing or centre of pressure trajectory). During the past few years, we proposed several algorithms to extract the timing and symmetry information. More research is needed to extract the bouncing and centre of pressure trajectory. We will also propose approaches to auto calibrate these systems for any individuals regardless of their weight and type of mattress used. Microphone arrays are able to localize and track various speakers, thus removing the undesired background noise and interference. Audio classification algorithms will be used to classify various speakers and perform important security and health monitoring tasks. Smart phones and tablets can play an important role in data acquisition, data transmission, context awareness and information presentation to the users. This project will use the internal sensors of a smart phone to monitor daily living activity. It will also use the phone to collect data from various sensors using their bluetooth or wifi communication links, and send it to the appropriate servers through the wifi or cellular networks. Smart phones can provide important context awareness information. For example, the internal microphone can characterize the ambient acoustic environment and classify various talkers. The internal ambient light sensor and temperature sensor can provide additional context information while the internal clock can provide the required synchronization. The accelerometers, gyroscope and GPS could monitor physical activity and exercise compliance. The next generation of smart phones will likely be equipped with more advanced sensors such as smell sensors. Smart phones or tablets can also perform some pre-processing and could convey security risks to authorities or clinical information to healthcare professionals.In summary, this project will propose novel algorithms for processing multiple data streams to extract specific information and present it to the users at the appropriate level in the appropriate format.

该研究计划的主要目标是设计智能的上下文感知数据处理算法，以从海量数据流中提取有用的信息。我们将利用来自环境的背景信息来改善系统的性能。在过去的几年里，半导体和微电子机械系统(MEMS)领域的进步导致了新型紧凑型传感器的引入，如压力传感器、加速计、陀螺仪、麦克风阵列和电子鼻。这些进步，加上强大的处理器和嵌入式无线接口，使得以最低功耗引入智能传感器系统成为可能。该项目将提出新的噪声消除、特征提取、模式识别和分类方法，以从多个传感器中提取有用信息。这些传感器必须协同工作，在正确的时间以正确的格式向用户提供所需的信息。该项目将考虑将固定式传感器和基于智能手机的传感器用于两个应用：智能家居设计和音频信号处理。固定传感器通常部署在特定环境中，如智能家居或关键基础设施。在这个项目中，我们将研究几种传感器技术，特别是压敏阵列和麦克风阵列。压敏阵列测量其表面上任何点施加的压力。当嵌入地板时，这些传感器能够检测入侵者，或者出于生物识别或健康诊断的目的执行步态分析。当放置在床垫下时，可以使用先进的信号处理算法来提取床位占用、呼吸或床位出口特征(定时、对称性、弹跳或压力中心轨迹)。在过去的几年里，我们提出了几种算法来提取时间和对称性信息。需要更多的研究来提取弹跳和压力中心轨迹。我们还将提出为任何个人自动校准这些系统的方法，无论他们的体重和所用床垫的类型。麦克风阵列能够定位和跟踪各种扬声器，从而消除不需要的背景噪声和干扰。音频分类算法将用于对各种扬声器进行分类，并执行重要的安全和健康监测任务。智能手机和平板电脑可以在数据采集、数据传输、上下文感知和信息呈现等方面发挥重要作用。该项目将使用智能手机的内部传感器来监控日常生活活动。它还将使用手机通过蓝牙或WiFi通信链路从各种传感器收集数据，并通过WiFi或蜂窝网络将数据发送到适当的服务器。智能手机可以提供重要的情境感知信息。例如，内部麦克风可以表征环境声学环境并对各种说话者进行分类。内部环境光传感器和温度传感器可以提供附加的上下文信息，而内部时钟可以提供所需的同步。加速计、陀螺仪和GPS可以监测身体活动和锻炼依从性。下一代智能手机可能会配备更先进的传感器，如嗅觉传感器。智能手机或平板电脑也可以执行一些预处理，并可能向当局传递安全风险或向医疗专业人员传递临床信息。综上所述，该项目将提出处理多数据流的新颖算法，以提取特定信息，并以适当的格式在适当的级别将其呈现给用户。