Distinguishing Between Human Activities in Real-Time Based on Wearable Sensor Data Using a Low-dimensional Model of Human Movement

使用人体运动的低维模型，基于可穿戴传感器数据实时区分人体活动

• 批准号: 1462773
• 负责人: Prashant Mehta
• 金额: $ 34.66万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462773&HistoricalAwards=false
• 关键词: Distinguishing; Between; Human; Activities; Real

Human Activity Recognition is the process of inferring human activity from motion sensors such as accelerometer and gyroscopes worn on the human body. These motion sensors are embedded in physical activity tracking devices and SmartWatches. Accurate inference of human activity offers many benefits for health monitoring and for promoting overall wellness of an individual. There are many machine learning-based approaches to human activity recognition from sensor data. However, in practice, the current approaches often suffer from poor accuracy on account of uncertainty due to wide range of human body types, sensor locations on the body and real-time sources of uncertainty such as changes in activity speed and intensity, sensor noise, packet drops, sensor failure etc. This award supports fundamental research to provide needed knowledge for the development of robust activity recognition methodology and algorithms. It is projected that there will be a trillion embedded sensors in connected people and devices by 2020. This project's algorithmic and software tools can potentially be directly applied to fitness monitoring, eldercare support, long-term preventive and chronic care, and rehabilitation. Therefore, results from this research will benefit the US economy and society. To promote transitions, several educational initiatives are planned that seek to engage undergraduate students in entrepreneurship. A major objective of the research concerns development of methods and algorithms to mitigate uncertainty in machine learning problems, such as the activity recognition problem, involving dynamic data sets. A control-theoretic framework is planned to not only address the robustness issues due to uncertainty, but also enable certain unified architectures for learning patterns from sensor data. If successful, the work can lead to novel algorithmic approaches to represent, learn and recognize hidden low order patterns in unstructured dynamic data sets. Besides methodological developments, this project will engineer other more tangible outcomes such as the development of algorithms and software for feedback particle filter, enunciation of control architectures and algorithms for representation of complex patterns in data, and development of software tools for the human activity recognition system.

人体活动识别是从人体上佩戴的加速度计和陀螺仪等运动传感器推断人体活动的过程。 这些运动传感器嵌入在身体活动跟踪设备和智能手表中。 人类活动的准确推断为健康监测和促进个体的整体健康提供了许多益处。 有许多基于机器学习的方法可以从传感器数据中识别人类活动。 然而，在实践中，当前的方法通常由于由于大范围的人体类型、身体上的传感器位置以及不确定性的实时源（诸如活动速度和强度的变化、传感器噪声、丢包、该奖项支持基础研究，为开发强大的活动识别方法提供所需的知识，算法 据预测，到2020年，将有1万亿个嵌入式传感器连接到人和设备。该项目的算法和软件工具可以直接应用于健康监测、老年人护理支持、长期预防和慢性护理以及康复。 因此，这项研究的成果将有利于美国的经济和社会。 为了促进过渡，计划采取一些教育举措，寻求让本科生参与创业。 研究的一个主要目标是开发方法和算法，以减轻机器学习问题中的不确定性，例如涉及动态数据集的活动识别问题。 一个控制理论框架计划不仅解决由于不确定性的鲁棒性问题，但也使某些统一的架构从传感器数据的学习模式。 如果成功的话，这项工作可能会导致新的算法方法来表示，学习和识别隐藏的低阶模式在非结构化的动态数据集。 除了方法的发展，该项目还将设计其他更切实的成果，如反馈粒子滤波器的算法和软件的开发，控制架构和算法的阐述，用于表示数据中的复杂模式，以及人类活动识别系统的软件工具的开发。