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

Abstract

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.

Identification of Violent Response using Feature Extraction Matrix Algorithm of a Time Series Data (FEM)

“…For activity analysis, these frames were considered. For each frame type, a total of 8,000 to 10,000 data frames are reported and 1,500 sample frames are randomly chosen, consisting of one set of all acts [66][67] . Various pre-processing techniques are used to extract the meaningful data from the raw information such Principal component analysis (PCA), Multi-regression analysis (MRA) to ranked the highly correlated features as shown in Figure 4.…”

“…Various pre-processing techniques are used to extract the meaningful data from the raw information such Principal component analysis (PCA), Multi-regression analysis (MRA) to ranked the highly correlated features as shown in Figure 4. After pre-processing, different supervised training techniques have been used in the dataset such as Naïve Bayes (NB), Support Vector Machine (SVM) , Decision Tree (DT), K-Nearest Neighbor (K-NN) in the controlled and uncontrolled environment [66][67].…”

Motion Capture Algorithm for Students' Physical Activity Recognition in Physical Education Curriculum

“…In order to identify normal activity and brute force attack activity, Randhawa's team used decision tree, k-NN classifier, support vector machine and wearable inertial fabric sensor to conduct various experiments on machine learning (supervised) classification technology. The experimental results showed that the vector algorithm provided 97.6% accuracy and 0.85 seconds of computation time for activity classification [17]. To give feedback on the performance of body movements in physical activities, Ferreira et al proposed a validation system based on 2D human pose estimation networks and deep semantic features.…”