Recent advances in remote patient monitoring (RPM) systems can recognize various human activities to measure vital signs, including subtle motions from superficial vessels. There is a growing interest in applying artificial intelligence (AI) to this area of healthcare by addressing known limitations and challenges such as predicting and classifying vital signs and physical movements, which are considered crucial tasks. Federated learning is a relatively new AI technique designed to enhance data privacy by decentralizing traditional machine learning modeling. However, traditional federated learning requires identical architectural models to be trained across the local clients and global servers. This limits global model architecture due to the lack of local models heterogeneity. To overcome this, a novel federated learning architecture, FedStack, which supports ensembling heterogeneous architectural client models was proposed in this study. This work offers a protected privacy system for hospitalized in-patients in a decentralized approach and identifies optimum sensor placement. The proposed architecture was applied to a mobile health sensor benchmark dataset from 10 different subjects to classify 12 routine activities. Three AI models, ANN, CNN, and Bi-LSTM were trained on individual subject data. The federated learning architecture was applied to these models to build local and global models capable of state of the art performances. The local CNN model outperformed ANN and Bi-LSTM models on each subject data. Our proposed work has demonstrated better performance for heterogeneous stacking of the local models compared to homogeneous stacking. This work sets the stage to build an enhanced RPM system that incorporates client privacy to assist with clinical observations for patients in an acute mental health facility and ultimately help to prevent unexpected death.
翻译:远程病人监测系统(RPM)最近的进展可以承认人类测量生命迹象的各种活动,包括表面船只的微妙动作。人们越来越有兴趣通过应对已知的限制和挑战,如预测和分类生命迹象和身体运动等被认为至关重要的任务,将人工智能(AI)应用于这一卫生保健领域。联邦学习是一种相对较新的AI技术,旨在通过下放传统的机器学习模型来增强数据隐私。然而,传统的联邦学习需要在当地客户和全球服务器中培训相同的建筑模型,这限制了全球的隐私模型结构,因为缺乏当地模型的异质性。为了克服这一点,本研究中提出了一个新的突发的联邦学习结构,即FedStack,它支持混合建筑客户模型的组合。这项工作为住院病人提供了一个受保护的隐私系统,目的是通过分散使用传统的机器学习模型来增强数据隐私。拟议的结构适用于移动健康传感器基准数据集,从10个不同主题来对12个日常活动进行分类。三个AI模型、ANNF、CNN和Bi-LSTM就单个主题数据进行了培训。这个模型化的学习结构用于单个主题数据,即快速临床观测系统,这个系统在改进了每个客户的网络运行阶段,从而改进了BNNISLSM数据库。这个模型,从而将改进了BA 将改进了B工作模型和BSLSDM工作。这个模型用于改进了每个数据库的模型,从而将改进了BSDM工作,将改进了BA-SM数据库的模型用于改进了BSM工作。