# Smart Clothing Framework for Health Monitoring Applications
## 2 Wearable Technology and Smart Clothing
### Computational Approach on Wearable Technologies
#### 2.6.3 Machine Learning for Healthcare  
Machine learning (ML) is highly prevalent, especially for analysing health-related data in the case of making decisions by reducing the dimensionality of data to make it manageable. Firstly, feature extraction is a vital job for extracting and analysing signals, and is considered as the main feature for corresponding data. To process physiological signals such as ECG, Fourier Transform and Wavelet Transform are widely used [61]. ML techniques learn and communicate information from numerous sources and realize unseen relationships among the source factors [30]. Various popular ML algorithms including Neural networks (NN), Naive Bayes, k nearest neighbour (k-NN), Support Vector Machine (SVM), Decision Tree (DT), regression, clustering, Hidden Markov model (HMM), Gaussian Mixture Modelling (GMM), etc., are currently used for analysing and predicting human health conditions to support decision making in both normal and emergency situations. Predictive models are built by these algorithms due to their outstanding training speed, excellent memory capability and great accuracy in predicting health conditions. K-NN classifiers determine geometrical gaps between feature vectors from various classes. Their use is a straightforward, effective nonparametric method used for classification, regression, recognition, etc. [62]. SVM classifies data through a linear decision boundary to separate all data points in binary classes. In addition, Naive Bayes carries out data classification based on the uppermost probability of its belonging to individual classes. Neural networks can learn, recall and generalize the targeted data by adjusting weights to check and test models to predict the ultimate result [63]. Reinforcement learning works through trial and error when investigating data and improves outcomes mostly for people affected by chronic diseases [30]. [PERSON] and [PERSON] [64] have proposed supervised machine learning tools to generalize the findings of existing life cycle assessment studies (LCAs) and to develop an ML model for assessing the sustainability of clothing products easily and over their lifecycles with limited data. Nine various supervised machine learning algorithms were tested considering a five-fold cross validation. The average accuracy, weighted average, recall and precision, mean squared error (MSE), [PERSON]'s Tau-b (Tau-b), and ordinal classification index (OCI) were considered as the key parameters for each algorithm. It was observed from the results that an average accuracy of 0.90 or higher was achieved by bagged decision trees and random forest algorithms. However, this work is dedicated to creating a classification model for a category of single product, but the model could be extended to other product categories and/or to other datasets. A soft computing technique was applied to build a model that is capable of estimating the heart rate (HR) response to exercise of constant intensity and recovery [65]. Multilayer perceptron artificial neural networks (NN) were employed using raw heart rate time series data. The results showed a mean relative square error of the estimated HR values of the order of \(10^{-4}\) with an absolute error as 1.19 beats per minute, which is low. The performance of the model proved that the NN model is capable of predicting the HR response to any persistent workout strength. However, the authors provided only the computational method to monitor heart rate rather than providing a design framework of wearable technology. [PERSON] et al. [66] proposed \"SmartFit\", an interactive smartphone application which will recommend optimal fitting clothes by detecting the body shape of a consumer. The goal of this project was to solve clothing fitting issues, convenience and improve online garment shopping experiences for consumers. Image processing and machine learning (SVM, NN, KNN) were employed to analyse the smartphone images.