Environment Feature Extraction and Classification for Context Aware Physical Activity Monitoring

ABSTRACT:

                Context aware Physical Activity (PA) monitoring of humans is important for the study of crime against children. This paper introduces a wearable context aware PA monitoring device which determines if the user is indoors or outside in situations of disrupted Global Positioning System (GPS) reception. In addition to a GPS sensor, multiple light and temperature sensors were added to our PA monitoring device. Differences in inside and outside temperature and the intensity of light are used to distinguish the context of location. Location, Light and temperature values were recorded using a controlled route during a period of 20 days in January and February. One of the non-parametric pattern recognition techniques (K-nearest neighbors) was used to classify indoor and outdoor conditions based on the combination of sensor values. Recently, all over the world, crime against children is increasing at higher rates and it is high time to offer safety support system for the children going to schools. This paper focuses on implementing children tracking system for every child attending school. However the existing systems are not powerful enough to prevent the crime against children since these systems give information about the children group and not about each child resulting in low assurance about their child safety to parents and also does not concentrate on sensing the cry of the child and intimating the same to its parents.