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Architectural concepts for managing biomedical sensor data utilised for medical diagnosis and patient remote care.

机译:用于管理用于医学诊断和患者远程护理的生物医学传感器数据的体系结构概念。

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This paper discusses experiences and architectural concepts developed and tested aimed at acquisition and processing of biomedical data in large scale system for elderly (patients) monitoring. Major assumptions for the research included utilisation of wearable and mobile technologies, supporting maximum number of inertial and biomedical data to support decision algorithms. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Architecture variants contained several schemes for data processing moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile up to server facilities. Analysis of transmission and processing delays provided architecture variants pros and cons but most of all knowledge about applicability in medical, military and fitness domains. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Sensor data processing persist the original data on handhelds but is mainly aimed at extracting chosen set of signal features calculated for specific time windows – varying for analysed signals and the sensor data acquisition rates. Long term monitoring of patients requires also development of mechanisms, which probe the patient and in case of detecting anomalies or drastic characteristic changes tune the data acquisition process. This paper describes experiences connected with design of scalable decision support tool and evaluation techniques for architectural concepts implemented within the mobile and server software.
机译:本文讨论了针对大型系统中老年人(患者)监测的生物医学数据的获取和处理而开发和测试的经验和体系结构概念。该研究的主要假设包括可穿戴和移动技术的利用,支持最大数量的惯性和生物医学数据以支持决策算法。尽管医学诊断和决策算法并不是研究的主要目标,但这个初步阶段对于测试现有的现成技术的功能以及系统逻辑组件的功能职责至关重要。体系结构变体包含几种数据处理方案,将信号特征提取,数据分类和模式识别的责任从可穿戴设备转移到移动设备,再到服务器设施。传输和处理延迟的分析提供了体系结构的优缺点,但最重要的是有关医疗,军事和健身领域的适用性的知识。为了评估和构建体系结构,已定义了一组替代技术堆栈和定量方法。已经定义了主要架构特征(高可用性,可伸缩性,可靠性),其中包括对传感器数据的异步处理,有效的数据表示,迭代报告,事件驱动的处理,限制拉动操作。传感器数据处理将原始数据保留在手持设备上,但主要目的是提取针对特定时间窗口计算出的选定信号特征集-随分析信号和传感器数据采集速率而变化。对患者的长期监视还需要开发机制,该机制可以对患者进行探测,并在检测到异常或剧烈特征变化的情况下调整数据采集过程。本文介绍了与可扩展决策支持工具和评估技术的设计有关的经验,这些工具和评估技术用于在移动和服务器软件中实现的体系结构概念。

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