大数据分析和物联网技术在智能主动式机器人股骨头切除术中的应用

Zlata Jelačić, Haris Velijević
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引用次数: 1

摘要

随着物联网(IoT)生态系统的出现和越来越多的使用,随之而来的是,技术可以找到改善人类许多条件的位置的机会也在增加。然而,这并不是什么新鲜事——纵观历史,我们一直在完善工具的使用,以帮助我们的日常生活。真正的进化在于我们和我们创造的工具之间的互动。工具现在是智能设备,这为我们提供了一个机会,即人与设备的交互让我们了解如何改进这种特定的合成。从改善我们的健康状况到检测创伤性脑损伤患者的心动过缓和反应,我们已经到了能够对我们的健康和状况的许多方面获得可操作的见解的地步。这在了解每个人的独特构成方面创造了一定的自主权,此外还产生了卫生从业者可以用来帮助诊断、确定医疗方法以及正确的康复和随访方法的信息。所有这些都有两个主要因素支持:物联网平台和大数据分析(BDA)。本文深入探讨了支持改善人类状况所需的物联网平台和BDA框架的示例设置。我们的SmartLeg假肢设备将先进的假肢和机器人技术与最先进的机器学习算法相结合,能够使假肢的工作适应最佳步态和功耗模式,从而为特定用户定制设备提供了手段。
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Application of Big Data Analysis and Internet of Things to the Intelligent Active Robotic Prosthesis for Transfemoral Amputees
With the advent and rising usage of Internet of Things (IoT) eco-systems, there is a consequent, parallel rise in opportunities where technology can find its place to improve a number of human conditions. However, this is nothing new - we have been perfecting the usage of tools to aid our daily living throughout history. The true evolution lies in the interaction between us and the tools we create. Tools are now smart devices, yielding an opportunity where human-device interaction is giving us the very knowledge on how to improve that particular synthesis. From improving our fitness to detecting bradycardia and response of traumatic brain injury patient, we have come to a point where we are able to gain actionable insight into a lot of aspects of our health and condition. This creates a certain autonomy in understanding the unique make-up of every single person, in addition to yielding information that can be used by health practitioners to help in diagnosis, determination of medical approach and right recovery and follow-up methods. All of this supported by two major factors: IoT platforms and Big Data Analysis (BDA). This paper takes a deep dive into exemplary set-up of IoT platform and BDA framework necessary to support the improvement of human condition. Our SmartLeg prosthetic device integrates advanced prosthetic and robotic technology with the state-of-the-art machine learning algorithms capable of adapting the working of the prosthesis to the optimal gait and power consumption patterns, which provide means to customize the device to a particular user.
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