论文标题
下一代医疗保健的智能非侵入性实时人类活动识别系统
An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare
论文作者
论文摘要
在人工智能(AI)驱动的医疗系统领域,人类运动检测引起了极大的关注。人类运动可用于通过识别瀑布,步态和呼吸障碍等特定运动来为弱势群体提供远程医疗解决方案。这可以使人们生活更独立的生活方式,并且在需要更多直接护理的情况下仍然具有监控的安全性。目前,可穿戴设备可以通过在一个人的身体上部署设备来实时监控。但是,将设备一直贴在一个人的身体上,使其不舒服,而且老年人除了一直被追踪的不安全感外,还倾向于忘记它穿着。本文演示了如何使用非侵入性方法在准实时场景中检测到人类运动。无线信号中的模式呈现出特定的人体运动,因为每种运动都会引起无线介质的独特变化。这些更改可用于识别特定的身体运动。这项工作产生了一个数据集,该数据集包含使用软件定义的无线电(SDR)获得的无线电波信号模式,以确定受试者是作为测试用例而站立还是坐下。该数据集用于创建机器学习模型,该模型用于开发的应用程序中,以提供标准或坐状状态的准实时分类。机器学习模型能够使用10倍的交叉验证使用随机森林算法实现96.70%的精度。将可穿戴设备的基准数据集与提议的数据集进行了比较,结果表明该数据集的准确性相似,近90%。本文开发的机器学习模型进行了两种活动测试,但已开发的系统设计并适用于检测和区分X活动数量。
Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real time monitoring by deploying equipment on a person's body. However, putting devices on a person's body all the time make it uncomfortable and the elderly tends to forget it to wear as well in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals presents particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine learning model was able to achieve 96.70 % accuracy using the Random Forest algorithm using 10 fold cross validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90 %. The machine learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.