Anomaly Detection Using Smart Shirt and Machine Learning: A Systematic Review Chapter Conference Paper uri icon


  • In recent years, the popularity and use of Artificial Intelligence (AI) and significant investments in the Internet of Medical Things (IoMT) will be common to use 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 that 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) to identify what type of anomaly the smart shirt can detect; (ii) what ML techniques are in use; (iii) which datasets are in use; (iv) identify smart shirt or signal acquisition devices worn in the chest region; (v) list the performance metrics used to evaluate the ML model; (vi) the results of the techniques in general; (vii) types of ML algorithms are 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.

publication date

  • 2022