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

Abstract

There are several women safety devices in the market today. Women who suffer these atrocities are even denied basic human rights, as set out in the Criminal Code. Women who are not as fit (physically) as men need to be protected from the evils of society. The introduction of actions and procedures for healthier women is not adequate and needs to be well improved. However, these devices are not fool- proof. The crux of this paper is a partial result of a challenging problem faced during design and construction a fool-proof Smart Jacket for women’s safety using fabric sensors. The smart jacket is envisioned for the women to wear on all occasions. An alert message and subjects’ Geo-location is sent to pre-assigned phone number if the subject is faced with violent situation. The jacket consists of fabric Sensors, Accelerometer, Gyroscope and Magnetometer, which are strategically placed to record maximum variations in signal for minimum movement in subject’s body. The primary challenge is not in design or construction of a jacket, but in accurately classifying violent activity from animated activity. Both violent activity and animated activities have commonality in sensor excitation. There are subtle differences which needs to be extracted to train a Machine learning algorithm to learn these patterns. Multivariate Regression Analysis (MRA) is used to highlight explanatory variables that can produce better convergence. With MRA results signifying a high degree of noise in the data, a newer approach, wherein, the ordinality of the data was ignored and cardinality of the data was considered for analysis. Machine learning approaches is then used to build a model using features extracted through MRA. Different machine learning techniques have been applied to classify the violent attack from the different activities such as stationary subject, walking, dancing. This study present that Support vector machine shows the better classification accuracy, computation time as compared to other algorithms.