论文标题
基于机器学习的智能衬衫的异常检测：系统评价
Machine Learning based Anomaly Detection for Smart Shirt: A Systematic Review
论文作者
论文摘要
近年来，人工智能（AI）和对医疗事物的大量投资（IOMT）的普及和使用，使用智能袜子，智能裤和智能衬衫等产品通常是常见的。这些产品被称为智能纺织品或电子纸质，它具有监视和收集我们身体发出的信号的打招呼。这些信号使得使用机器学习（ML）技术在ThisArea中起着至关重要的作用来提取分量。这项研究对使用智能衬衫中的MML技术进行了对文献（SLR）的系统评价（SLR）。 SLR的目标是：（i）确定哪种类型的异常智能衬衫； （ii）使用了哪些ML技术； （iii）使用了哪些数据集； （iv）识别智能衬衫或信号采集设备； （v）列出用于评估ML模型的性能指标； （vi）一般技术的结果； （vii）正在使用ML算法的类型。SLR选择了2017 - 2021年间发表的11项主要研究。结果表明，鉴定了6种类型的异常，跌落异常是引用最多的异常。支持Vectormachines（SVM）算法最常使用。大多数主要研究都使用了公共或私人数据集。最引用了Hexoskin智能衬衫。最常用的度量性能是准确性。 ONAVENE，几乎所有基本研究均呈现出90％以上的结果，所有基本研究都使用了ML的thesupervision类型。
In recent years, the popularity and use of Artificial Intelligence (AI) and large investments on theInternet of Medical Things (IoMT) will be common to use products such as smart socks, smartpants, and smart shirts. These products are known as Smart Textile or E-textile, which has theability to monitor and collect signals that our body emits. These signals make it possible to extractanomalous components using Machine Learning (ML) techniques that play an essential role in thisarea. This study presents a Systematic Review of the Literature (SLR) on Anomaly Detection usingML techniques in Smart Shirt. The objectives of the SLR are: (i) to identify what type of anomalythe smart shirt; (ii) what ML techniques are being used; (iii) which datasets are being used; (iv)identify smart shirt or signal acquisition devices; (v) list the performance metrics used to evaluatethe ML model; (vi) the results of the techniques in general; (vii) types of ML algorithms are beingapplied.The SLR selected 11 primary studies published between 2017-2021. The results showed that6 types of anomalies were identified, with the Fall anomaly being the most cited. The Support VectorMachines (SVM) algorithm is most used. Most of the primary studies used public or private datasets.The Hexoskin smart shirt was most cited. The most used metric performance was Accuracy. Onaverage, almost all primary studies presented a result above 90%, and all primary studies used theSupervisioned type of ML.
