Violent activity recognition by E-textile sensors based on machine learning methods

In the new era of technology with the development of wearable sensors, it is possible to collect data and analyze the same for recognition of different human activities. Activity recognition is used to monitor humans' activity in various applications like assistance for an elderly and disabled person, Health care, physical activity monitoring, and also to identify a physical attack on a person etc. This paper presents the techniques of classifying the data from normal activity and violent attack on a victim. To solve this problem, the paper emphasis on classifying different activities using machine learning (supervised) techniques. Various experiments have been conducted using wearable inertial fabric sensors for different activities. These wearable e-textile sensors were woven onto the jacket worn by a healthy subject. The main steps which outline the process of activity recognition: location of sensors, pre-processing of the statistical data and activity. Three supervised algorithmic techniques were used namely Decision tree, k-NN classifier and Support Vector Machine (SVM). Based on the experimental work, the results obtained show that the SVM algorithm offers an overall good performance matched in terms of accuracy i.e. 97.6% and computation time of 0.85 seconds for k-NN and Decision Tree for all activities.