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首页> 外文期刊>Biomedical and Health Informatics, IEEE Journal of >Context-driven, Prescription-Based Personal Activity Classification: Methodology, Architecture, and End-to-End Implementation
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Context-driven, Prescription-Based Personal Activity Classification: Methodology, Architecture, and End-to-End Implementation

机译：上下文驱动的基于处方的个人活动分类：方法论，体系结构和端到端实施

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摘要

Enabling large-scale monitoring and classification of a range of motion activities is of primary importance due to the need by healthcare and fitness professionals to monitor exercises for quality and compliance. Past work has not fully addressed the unique challenges that arise from scaling. This paper presents a novel end-to-end system solution to some of these challenges. The system is built on the prescription-based context-driven activity classification methodology. First, we show that by refining the definition of context, and introducing the concept of scenarios, a prescription model can provide personalized activity monitoring. Second, through a flexible architecture constructed from interface models, we demonstrate the concept of a context-driven classifier. Context classification is achieved through a classification committee approach, and activity classification follows by means of context specific activity models. Then, the architecture is implemented in an end-to-end system featuring an Android application running on a mobile device, and a number of classifiers as core classification components. Finally, we use a series of experimental field evaluations to confirm the expected benefits of the proposed system in terms of classification accuracy, rate, and sensor operating life.
机译：由于医疗保健和健身专业人员需要监视运动的质量和依从性，因此对一系列运动活动进行大规模监视和分类至关重要。过去的工作尚未完全解决扩展带来的独特挑战。本文针对这些挑战提出了一种新颖的端到端系统解决方案。该系统基于基于处方的上下文驱动的活动分类方法。首先，我们表明，通过完善上下文的定义并引入场景的概念，处方模型可以提供个性化的活动监视。其次，通过从接口模型构建的灵活体系结构，我们演示了上下文驱动分类器的概念。上下文分类是通过分类委员会的方法实现的，而活动分类则是根据上下文特定的活动模型进行的。然后，该架构在端到端系统中实现，该系统具有在移动设备上运行的Android应用程序以及许多分类器作为核心分类组件。最后，我们使用一系列实验现场评估来确认拟议系统在分类​​精度，速率和传感器使用寿命方面的预期收益。

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