mlsanityData to 7 Oct 2026

Machine Learning based Anomaly Detection for Smart Shirt: A Systematic Review

arXiv 2203.03300 ↗First posted 7 Mar 2022cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 59 references that are in the map. First posted on arXiv 7 Mar 2022.

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References · 6

  1. 2017Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigationcited by 74
  2. 2019Real-Time Reinforcement Learningcited by 38
  3. 2015The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Reviewcited by 27
  4. 2018Deep Reinforcement Learning for General Video Game AIcited by 25
  5. 2021Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcarecited by 15
  6. 2021Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifierscited by 1

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