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An Intelligent Information Forwarder for Healthcare Big Data Systems With Distributed Wearable Sensors

机译:具有分布式可穿戴传感器的医疗大数据系统智能信息转发器

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An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed.
机译:越来越多的老年人希望过独立的生活方式,而不是依赖侵入式护理计划。提出了一种使用可穿戴传感器的大数据解决方案,该传感器能够对老年人进行连续监控,在必要时提醒相关护理人员,并将相关信息转发到大数据系统进行分析。这种解决方案面临的挑战是通过多维,动态和非线性传感器读数来发展上下文感知,这些传感器读数与可观察到的人类行为和健康状况之间的相关性较弱。为了解决这一挑战,本文讨论了具有智能数据转发器的可穿戴传感器系统。转发器采用隐马尔可夫模型进行人类行为识别。局域性哈希算法是一种学习传感器模式的有效机制。实施了原型解决方案以监视分散用户的健康状况。结果表明,智能转发器可以为远程传感器提供上下文感知。当某些行为发生时,它们仅将重要信息传输到大数据服务器进行分析,并避免过多的通信和数据存储。该系统功能完好,同时让用户放心,因为他们正在监视和分析他们的安全性。

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