Physical Activity Classification Using a Smart Textile
Nour Cherif, Youssef Ouakrim, Amel Benazza‐Benyahia, Neila Mezghani · 2018
The aim of this study is to develop a human activity classification system based on a wearable intelligent textile and machine learning techniques. Using the Relief-F feature selection algorithm, we identified a set of relevant features collected by the smart textile. Then, the retained features have fed a classifier in order to recognize the underlying activity. In this respect, we test a support vector machine classifier (SVM) and a k-nearest neighbor classifier (KNN). The results show the reliability of the feature selection procedure and indicate that the activities can be recognized with an overall accuracy of more than 96.37 % using the KNN classifier and 95.4 % using the SVM classifier. Since the Hexoskin intelligent textile also allows the collection of physiological data, these experimental results are very promising for practical applications of acquisition of human activities recognition, which will make it possible to study the patient's state of health or to detect physiological abnormalities in real time depending on the physical activity exerted.