Anomaly Detection Using Smart Shirt and Machine Learning: A Systematic Review

Abstract

AbstractIn recent years, the popularity and use of Artificial Intelligence (AI) and significant investments in the Internet of Medical Things (IoMT) will be common for products such as smart socks, smart pants, and smart shirts. These products are known as Smart Textile or E-textile, which can monitor and collect signals our body emits. These signals allow it to extract anomalous components using Machine Learning (ML) techniques that play an essential role in this area. This study presents a Systematic Literature Review (SLR) on Anomaly Detection using ML techniques in Smart Shirt. The objectives of the SLR are: (i) identify machine learning techniques for anomaly detection in the smart shirt; (ii) identify the datasets used to train the ML algorithm; (iii) identify smart shirts or devices for acquiring vital signs; (iv) identify the performance metrics for evaluating the ML model; (v) types of ML being applied. The SLR selected eleven primary studies published between January/2017-May/2022. The results showed that six anomalies were identified, with the Fall anomaly being the most cited. The Support Vector Machines (SVM) algorithm is the 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. Almost all primary studies presented a result above 90%, and all primary studies used the Supervisioned type of ML.KeywordsMachine learningAnomaly detectionSmart shirtSmart textileSystematic review