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Organic Computing Techniques for Run-Time Self-Adaptation of Ubiquitous, Multi-Modal Activity Recognition Systems

用于普遍存在的多模态活动识别系统运行时自适应的有机计算技术

基本信息

批准号：
276698135
负责人：
Professor Dr. Paul Lukowicz
金额：
--
依托单位：
Fachgebiet Intelligente Eingebettete Systeme
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2018-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/276698135?language=en
关键词：
Organic Computing Techniques Run Time

项目摘要

Ubiquitous activity and context recognition (AR) aims at translating information provided by simple sensors into high level knowledge about human activities and the situation in the environment. Over the last decade researchers have shown the principle feasibility of recognizing activities and situations ranging from the steps of a maintenance task, through every day activities at home, to sport and social interactions. A major limitation of today's state of the art approaches is that they mostly assume system configurations exactly defined at the system's design-time that remain fixed at run-time. Thus, for each application, the user needs to place specific sensors at certain well-defined locations in the environment and on his body. All stages of the signal processing chain (from signal conditioning through feature selection to classification) are then custom-designed for the concrete configuration and task. While such static runtime setups can be guaranteed under controlled laboratory conditions, the possibility of sensors dropping out and new sensors appearing must be taken into account in real world settings. In this proposal we will develop new Organic Computing (OC) techniques to facilitate self-healing (when a sensor drops out) and self-improvement (when a new sensor appears) for ubiquitous activity and context recognition systems. Specifically, we will develop a layered Observer/Controller architecture where the System under Observation and Control (SuOC) is (are) human(s) in an intelligent, sensor enabled environment. The bottom layer (reaction layer) can be seen as a blueprint of standard AR based context sensitive systems. The adaptation layer enables the system to improve autonomously -- or semi-autonomously with sporadic human feedback -- the classifier at the reaction layer using the new sensor information or to adapt it a sensor drops out. In general, autonomous adaptation methods cannot guarantee to always lead to an improvement and, in special cases, they can even result in performance degradation. Thus, the potential gains and the risks of a possible adaptation are estimated and considered not only at the adaptation layer, but also at the reflection layer (top layer) that models the long term system evolution to ensure that continuous modifications of the system configuration lead to long term improvement and not to un-bounded performance degradation of the overall system. In our approach we develop new OC techniques for AR by combining and extending methods from Machine Leaning, Pattern Recognition, and related fields (in particular generative and discriminative modeling, semi-supervised learning, active learning, and nonlinear dynamic systems theory). We will evaluate our methods on existing large scale AR data sets.

泛在活动和上下文识别（AR）旨在将简单传感器提供的信息转换为有关人类活动和环境状况的高级知识。在过去的十年中，研究人员已经证明了识别活动和情况的原则可行性，从维护任务的步骤，到日常活动，再到体育和社会互动。当今最先进的方法的一个主要限制是，它们大多假设在系统的设计时精确定义的系统配置在运行时保持固定。因此，对于每个应用程序，用户需要将特定的传感器放置在环境中和身体上的某些明确定义的位置。信号处理链的所有阶段（从信号调理到特征选择再到分类）都是针对具体配置和任务定制设计的。虽然在受控的实验室条件下可以保证这种静态运行时间设置，但在真实的世界设置中必须考虑传感器退出和新传感器出现的可能性。在这项提案中，我们将开发新的有机计算（OC）技术，以促进无处不在的活动和上下文识别系统的自我修复（当传感器退出）和自我改进（当一个新的传感器出现）。具体来说，我们将开发一个分层的观察者/控制器架构，其中观察和控制下的系统（SuOC）是（是）人（S）在一个智能的，传感器启用的环境。底层（反应层）可以被视为基于标准AR的上下文敏感系统的蓝图。自适应层使系统能够自主地-或半自主地与零星的人类反馈-使用新的传感器信息的反应层的分类器或适应它的传感器退出。一般来说，自主自适应方法不能保证总是导致改进，在特殊情况下，它们甚至可能导致性能下降。因此，不仅在适应层而且在对长期系统演进进行建模的反射层（顶层）处估计和考虑可能的适应的潜在增益和风险，以确保系统配置的连续修改导致长期改进而不是整个系统的无限性能退化。在我们的方法中，我们通过结合和扩展机器学习，模式识别和相关领域（特别是生成和判别建模，半监督学习，主动学习和非线性动态系统理论）的方法，开发了新的OC技术。我们将在现有的大规模AR数据集上评估我们的方法。